Noise Reduction Whitepaper

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FeaturingDne1.0

Effective Noise Reduction & Detail Optimization

An Analysis & Post Capture Processing Model

White Paper

nik multimedia, Inc.


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Table of Contents

Introduction

The Dependent Nature of Noise ............................................4

Dilemmas of Techniques.......................................................5Science Versus Art ...............................................................5

The Evolutionary Nature of Noise Reduction...........................6

The Proposed Solution Dne ...........................................6

An Introduction To Noise

Noise and its Origins ............................................................ 7

Identifying Noise and Common Terms.................................... 7

Shot Noise .........................................................................8

Read Noise ........................................................................8

Fixed Pattern Noise..............................................................8

Other Common Terms..........................................................9

Luminance Versus Chrominance Noise ..................................9

Identifying Noise..................................................................9

Identifying Noise Using Photoshop Techniques..................... 10

Noise and the Camera .......................................................10

Image Detail and its Relationship in Noise Reduction............. 11

Removing Versus Reducing Noise ....................................... 11

Common Methods For Addressing Noise

Dealing with Fixed Pattern Noise......................................... 12

Working with Aberrant Hot Pixels ........................................ 12Common Methods for Reducing Hot Pixels ........................... 13

The Threshold Dilemma ....................................................14

Reducing Noise While Sharpening......................................14

Technical Methodologies For Noise Reduction

Blur Variations................................................................... 16

Median Filter..................................................................... 17

Fourier Transformation ....................................................... 18

In-Camera Noise Management: Practical ConsiderationsAdvantages....................................................................... 19

Issues & Challenges ............................................................20

The Permanent Nature of Noise Reduction............................20

Double Processing and Workow ........................................20

The Need for Processing Power..........................................20


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Undesirable Effects of Reducing Noise

Blind Area Artifacting ........................................................ 21

Detail, Noise Relationships, and Blind Area Artifacting ...........22

Contrasting Noise and Detail...............................................22

Remaindered Pixels ...........................................................23

Painterly Effect..................................................................24Blurring Effect ...................................................................25

Resolution Issues: Screen Versus Print Images ...................... 25

Print Optimized Versus Screen View Presentation.................... 25

The Non-Scientic and Subjective Nature of Perception ........ 26

Details and the Power of the Human Eye ............................. 27

Practical & Subjective Issues in Noise Reduction ..................... 27

Optimized Noise Reduction

1. Previewing and Analyzing the Image ...............................30

Auto-Detection of Noise in Sensitive Areas ...........................30Multi-Preview Mode ........................................................30

Analysis Mode: Screen-Based Image Analysis Tools ................. 31

Grab and Drop Preview.................................................... 31

2. Optimizing Images Based on a Specic Camera................ 31

The Unique Nature of Digital Cameras & Image Details ............ 31

The Detail-to-Color Correlation...........................................32

Targeted Reduction and the Camera Prole Controller............. 32

3. Reducing Color Noise and Maintaining Color Details .......... 33

Blurring Lab Channels Versus Dne Detail Protection.............. 33

4. Balancing Detail and Color in Noise Reduction.................. 35

The Relationship of Noise, Detail, and Artifacts ...................... 35

JPG Reprocessing for Artifact Reduction ............................... 36

5. Selectively Reducing Noise Quickly and Intuitively ............ 37

Selectively Removing Hot Pixels in Dark Scenes ..................... 37

Pressure Sensitivity & Selective Reduction ............................ 37

6. Optimizing Color Changes and Relationship ......................38

Color Corrections: Considering Detail and Noise ..................... 38

Color and Colorcast Adjustments .......................................38

7. Controlling Light and Contrast in Image Details................. 39

Tonal Corrections and Noise ..............................................39

Highlights & Shadows and Light Adjustments .........................39

Adjusting for Counter-Light .........................................40

Highlights & Shadows ................................................40

Dne Tonal Adjustments (Levels) ................................41

More Information ..............................................................42

Dne System Requirements .............................................42

Copyright 2002 2003 nik multimedia, Inc. All rightsreserved. Some implementations discussed in thispublication are proprietary in nature and included inpending patents. nik multimedia, the nik multimedialogo, Dne, and nik Sharpener Pro are trademarks ofnik multimedia, Inc. All other trademarks and brandnames are the property of their respective owners.

www.nikmultimedia.com


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Introduction

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Introduction

Noise reduction, from a photographic perspective, is a

problem without a universally accepted solution. There is no

single established routine, no one set of algorithms, no magic

bullet that reduces or eliminates noise while maintainingdetail. Reducing noise optimally involves a combination of

considerations that include the capture device (the camera),

the nature of noise and image details, and the non-scientic

nature of perception. With this said, the single answer to the

question of how noise should be reduced in an image isIt

depends.

The Dependent Nature of Noise

Noise reduction depends on so many variables that a single

mathematical process alone cannot reduce noise effectively.

Photographic details and the natural characteristics of the

image do not factor into one single algorithm or mathematical

solution. Optimal noise reduction, whatever the process, must

protect detail and maintain the natural appearance of the

entire image. Achieving that result depends on effectively

dealing with variables that originate at various points in the

imaging process, from capture to the nal presentation of the

image.

While some aspects of noise are predictable, others are

random. While some can be dealt with at the time of capture,

others must be dealt with post-capture methods. Dealing

with noise in the printed or projected image is a matter of

judgment. Considering this, noise reduction in the post-capture

stage becomes the most effective approach.

The current state of the art for the post-capture stage of

noise reduction lacks an effective solution that takes into

account both the dynamic nature of photographic detail as

well as the importance that workow plays in the creation of

a quality digital image. Many tricks and techniques are often

applied piecemeal to an image to deal with particular noise

problems in an image. Scripted, pre-dened, and recorded

image editing routines are popular remedies for noise, all of

which differ based on an individuals experience. The "tips and

tricks" approach to noise reduction is an acknowledgement of

the reality that there is no coherent process or combination of

Optimal noise reduction,

whatever the process, must

protect detail and maintain

the natural appearance of

the entire image.

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Introduction

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algorithms that can globally eliminate noise in an image.

Dilemmas of Techniques

While many of the tricks and techniques used in image editing

can be effective in reducing noise in particular images, they

commonly create undesirable side effects that destroy detail

and the effectiveness of an otherwise good image. Some are

better than others, but a single solution that effectively reduces

noise across the broadest range of images without creating

the unwanted side effects does not exist. In this paper, we

discuss a variety of techniques and methodologies as well as

their side effects. One broadly used technique discussed in this

paper is the use of a threshold. Certain implementations of

a threshold within a noise reduction solution present distinct

trade-offs. Other techniques, such as those that utilize blurring

or averaging approaches, soften or alter detail in order to mask

noise or substitute detail in the image.

A pragmatic approach to noise reduction considers the

image in the nal print and sets out to maintain the natural

photographic characteristics of the image while addressing the

problem of noise. This approach emphasizes the importance of

the quality of the image as dened by the details that appear

across the entire image in its nal stage, the print.

Science Versus Art

Effective photographic optimization of detail in a digital

image involves providing maximum detail presentation while

avoiding a digitally processed appearance. Reducing the noise-

to-detail ratio at the capture and signal processing stage is

device dependent, and many cameras address the problem

of noise reduction, some more effectively than others. At the

post-capture phase, however, managing an acceptable level

of noise reduction and maintaining optimal detail are opposing

concepts. At the pixel level, detail and noise are structurallythe same. Noise and detail are dened by how they are

perceived in the image. The human eye makes that distinction.

If the pixel distracts from the detail in the image, the detail

is unwanted in its current state; if the pixels contribute to

effective detail in the image, the noise may be perceived as

detail, and thus is an important part of the image. An effective

approach to noise reduction hinges on the perception of the

At the post-capture phase,

managing an acceptable

level of noise reduction

while maintaining optimal

detail are opposingconcepts, making optimal

noise reduction an even

further challenge.


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Introduction

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viewer, assessing the balance between the optimal level of

detail and the acceptable level of noise in the print. In a sense,

this concept is contrary to commonly used scientic methods.

Where most scientic approaches treat the signal (a necessary

step) and then process the image to reduce noise, the proposed

concept includes considerations from the signal processing stage

but reserves effective noise reduction and detail optimization

for the post-capture process. In other words, the proposed

solution considers the nal image and moves backwards to

the capture process to consider the sources and nature of

the noise in order to treat the image with a combination

of imaging science tools. In doing so, a combination of a

scientic approachusing effective tools to analyze and treat

the imageand visual tools will result in an optimal noise

reduction-detail optimization system.

The Evolutionary Nature of Noise Reduction

While some types of noise in a digital image are unavoidable,

given the nature of light and the technology of analog to digita

transfer, other kinds of noise arise from the capture device. The

larger and more advanced the cameras sensor (CCD or CMOS)

the lower the level of noise. As image sensors in cameras

and their technologies advance, noise and its appearance will

change.

The Proposed Solution Dne

Dne was developed based on a study of noise that focused

specically on how noise is manifested in the image. That is, we

approach noise from a photographic perspective and focus on

how noise appears in the image. We focus on noise and image

details from a photographic perspective and propose a solution

that is effective, considering the inevitable nature of noise and

the evolution of noise reduction in capture devices.

This paper evaluates digital noise, its role in the digital image,and methods for compensating for noise with a specic focus on

maintaining the photographic tendencies and natural balance

of a digital image, and introduces Dne as a solution for

effective noise reduction and detail optimization.

The manner in which

images are captured,

assembled, and processed

by digital cameras will

constantly evolve, making

it even more important forpost-capture noise solutions

to be able to evolve with

capture technology.


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Introduction to Noise

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An Introduction To Noise

Noise and its Origins

Noise in a digital image consists of visible errors, which are

created by an electrostatic charge within the camera and a

process that converts an analog signal to its digital elements.These errors are transferred to the image as part of the detail

of the image and appear as bright, colored, or dark specks. As

such, noise is actually detail in the image that does not appear

as it is expected due to an error in the image capture process.

Various factors affect noise, ranging from the presence of

light at the time of capture, exposure time, sensor (CCD or

CMOS) temperature, and the manner in which the cameras

sensor processes the image. These errors appear in print and

on screen as distracting aberrations which, when visible to

the human eye, distract the viewer and create an unnatural

appearance.

There are a variety of terms used to describe noise and their

sources. We will make distinctions about the nature of noise

based on its origin as well as its appearance in the nal image.

We make these distinctions to clarify the current state of

technology while acknowledging the necessary compromise

in balancing noise with optimal detail. Understanding the

origins of noise and how it appears in the image is the rst

step in determining how to reduce noise and create an

optimal, balanced image. The objective of this discussion is

to understand the nature of noise in digital photography, to

accept the current state of technology, and to encourage the

use of appropriate tools in optimizing workow to achieve the

best possible image.

Identifying Noise and Common Terms

Various terms are used to identify noise in a digital image.

Often the terms used to describe noise relate to their origins

while others relate to the appearance of noise. It is important

to make clear distinctions in dening terms and their

applications when discussing noise in the digital image. Grain,

for example, is often used to describe the appearance of noise

in a digital image, even when grain, a term derived from lm

images, is not a factor in the digital capture process.

Common terms like

grain are often misused

regarding noise reduction,

making the distinction

between noise reduction

and digital grain

reduction even more

difcult.


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Shot Noise

Shot noise is the most apparent type of digital image noise.

Shot noise is a pattern of dark, bright, or colorful specks that

is often best visible against plain areas, such as sky. Shot noise

will be present to some degree in every digital image because

of the random nature of light photons and sensor electrons.

Because of the random nature of light, when a digital image

is captured, not every sensor in the CCD will be struck by the

same amount of photons. Because shot noise is caused by light,

the more light, the more shot noise. However, an interesting

phenomenon occurs as light increases: the shot noise seems to

plateau because it tends to merge with Read Noise (explained

below) as they cancel each other out. As light increases, the

distraction from detail which shot noise causes in a portion

of an image will seem to be negligible to the viewer. Viewedacross the image as a whole, however, Shot Noise can be

distracting and appear unnatural to the viewer.

Read Noise

Read Noise is another common term that is used to identify

noise based on its source. Read Noise is generated by the

digital cameras processor and is analogous to the electronic

noise one might hear when listening to recorded music.

Sometimes called amp noise, Read Noise originates in the

processor and is generated by random electrons that excite the

pixels in the sensor array causing them to misre, resulting in

chrominance noise or luminance noise. Ironically, the better the

camera, the more powerful the amplier, the more powerful

the amplier, the more read noise. While the photographer has

no control over Read Noise caused by the amplier, two other

sources of read noise are within the photographers control.

Read Noise increases as the temperature inside the camera

increases. It also increases with longer exposures and with high

ISO ratings. Camera design that keeps CCD and CMOS sensorscool continue to address the problem of Read Noise with some

success.

Fixed Pattern Noise

Fixed Pattern Noise is another term frequently used to identify

a specic type of noise. Fixed Pattern Noise is the only noise

that is not completely random. Because the pixels in the sensor

Read Noise and the CCD

After each shot, the cameras sensors

are reset to a zero position. However,

this resetting process may not always

be uniform. The noise that is generated

is referred to as Reset Noise. When the

process of reading out the values of

the CCD extends over multiple clocks

(processing time units of the processor

that reads the CCD) the sensors are not

read simultaneously. When that occurs

noise can be created simply because

the time between capture and readout

is not uniform for the entire chip.


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array are not uniform in size, spacing, and efciency, very

slight errors are produced by each pixel each time an image

is captured. These pixel errors repeat themselves in each

photo taken, making this particular kind of noise created by an

individual camera predictable. This xed pattern noise will

vary only slightly in intensity from photo to photo, increasing as

light in the image decreases.

Other Common Terms

In coping with the problem of noise in digital images, users

have come up with a number of terms to describe what they

see in the image. The term Hot Pixel,for example, is used to

describe an obvious hot spot in the image. Hot Pixels can

appear as either bright white pixels or as colored spots in the

image and come from any error or misring of a pixel. The

term Hot Pixel is used more often to describe the appearance

of noise rather than its cause.

Luminance Versus Chrominance Noise

In addition to dening noise at its origins, we nd it useful to

dene noise as it appears in the image as Luminance Noise

and Chrominance Noise. Scientic papers and articles often

use these terms to refer to brightness and color. We use these

terms to distinguish noise based on its characteristics, which

is the way that we address the issue of noise. We dene noisewith no appearance of color as Luminance Noise, as it manifests

itself as purely dark or bright white noise. We use the term

Chrominance Noise, on the other hand, to describe noise that

consists of some degree of color.

These distinctions are important in the process of reducing

noise in the post capture process. As we evaluate and analyze

noise reduction, it is essential to identify noise as it appears

in the image, rather than how it is generated. This principle

becomes an important aspect of optimizing image details whilereducing noise effectively for the nal presentation of the

image.

Identifying Noise

Visually identifying noise in an image is an important aspect

of detail optimization and noise reduction. When images

are captured and viewed, noise often appears as subtle and

To effectively address

noise in the post-capture

stage of image editing, it is

essential to identify noiseas it appears in the image,

rather than how it was

generated.


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Noise often exists at the

same intensity level across

an image, while appearing

more dominant in onearea, such as sky, and less

apparent in foliage and

other high detail areas.

indistinguishable aspects of the image. This is due in part to

the fact that noise can appear in a combination of random and

xed characteristics with a range of intensity. In other words,

noise does exist in all digital images, and its impact on the

image needs to be considered visually as it relates to detail

in the image. And because detail is closely related to noise,

it becomes increasingly important to be able to locate and

identify noise. The crucial aspect of noise reduction in the post

capture process is identifying noise and distinguishing noise

from detail.

Identifying Noise Using Photoshop Techniques

There are a variety of basic techniques for identifying noise in

image editing applications. The following routine provides a

basic method for identifying noise in a digital image.

1.Open an image that has a low detail area, such as a sky or plain

background that has been captured in moderate to low light.

2.Crop the image to show the plain or low detail area.

3.Within Photoshop, access the High Pass lter from the Filter

menu (Filter 4 Other 4 High Pass) and apply the lter with a

radius of 3 pixels.

4.Hold Control + Shift + L keys to apply Auto Levels, which

increases the contrast of the High Pass, making details more

visible. At this point, both Chrominance and Luminance noise arevisible.

5.To isolate the Chrominance Noise and show the Luminance

Noise, hold Control + Shift + U.

Noise and the Camera

Theoretically, every camera captures images in the same way:

light strikes a grid of sensors and those sensors translate the

light into a digital image. The light waves (photons) that create

the image are not digital in naturethey either strike or do

not strike a sensor on the CCD or CMOS. The analog signal

coming into the camera must be converted to a digital signal

by an array of sensors in the back of the camera. The manner

in which a particular camera captures the signal and converts

it into a digital image determines to a large degree the type

and degree of noise which will be present when the image is

printed. Behind the CCD or CMOS lies electronics that dene,

order, and amplify the digital signal, causing particular kinds


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of noise. It is important to understand how shooting conditions

and the capture process affect noise. It is also important to

learn to recognize noise and its relationship to detail in the

postcapture noise reduction process.

Image Detail and its Relationship in Noise ReductionAs mentioned earlier, deciding what is considered noise and

what is considered detail in an image is crucial to a pragmatic

approach to noise reduction. Within the noise reduction

process, it is essential to judge noise, not on the characteristics

of the individual pixel, but rather in the context of detail in the

image. Focusing on the information at the pixel level denies the

context of the image, which offers essential information to the

treatment of detail. By looking at noise in context, in relation

to the detail surrounding it, we see the image more from a

photographic perspective.

While our approach to noise reduction and detail optimization

is based on a range of imaging science practices, the proposed

solution offered by Dne relies on photographic principles

which are applied from a post capture perspective and involve

key concepts related to digital imaging as well as photography.

Removing Versus Reducing Noise

From a photographic perspective, the concept of noise removal

is useless. Photographic detail and noise are inextricably linked

in every image.

As a result, the concept of noise removal is neither optimal

nor practical. Rather, reducing noise while balancing and

maintaining important detail leaves the image more natural.

Removing or eliminating the presence of noise is contrary

to obtaining a natural-looking photograph. Reducing versus

removing noise is a key distinction in effective noise reduction

and detail optimization that leads to better digital images.

From a photographic

perspective, the concept of

noise removalis useless.


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Common Post Capture Methods for Addressing Noise

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Common Methods For Addressing Noise

Noise reduction is a compromise that addresses the presence of

wanted versus unwanted detail. The following section outlines

common methods for reducing noise under certain conditions.

As with all methods and techniques for reducing noise in thepost-capture process, each addresses a limited range or type of

noise that occurs within the digital image.

Dealing with Fixed Pattern Noise

Fixed Pattern Noise, noise that is generated within the camera

itself, is a byproduct of all digital images. Fixed Pattern Noise

appears in images that were captured in any degree of light.

When present in dark or night shots, noise can often be even

more visible because of the dark background of the image.

Dark Frame Subtraction is a typical method for reducing Fixed

Pattern Noise in images that were captured under extreme low

light conditions. Dark Frame Subtraction can be an effective

method when targeting the Fixed Pattern Noise that occurs

within the camera due to long exposures under low light

conditions.

The Dark Frame Subtraction process is a lengthy and tedious

process that involves capturing the image, then creating a

dark and empty frame with the camera (shortly after the

initial capture), and then using the dark frame in the image

editing process to remove the pixel errors that occurred

when capturing the image. Dark Frame Subtraction has some

advantages for images captured under low light, but it is not a

solution for noise from other sources.

Working with Aberrant Hot Pixels

Hot Pixel is a general term used to describe bright or colored

specks in an image. As the term indicates, a hot pixel is a

pixel that is overcharged and misres, destroying detail in

the image. Generally, hot pixels appear in a xed pattern in

the image and appear more frequently in images that were

captured in low light conditions.

The pixels in the sensor array of a digital camera are all

sensitive to light to varying degrees, and some are more

sensitive than others. Open a shutter on any camera long

enough and some of the more sensitive pixels will begin to re.

Dark Frame Subtraction

uses an image capture

with a complete lack of

light, an empty image,to reproduce the pattern of

noise, which can be used to

replace or alter misred

pixels in the image.



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The longer the exposure, the more hot pixels, and the higher

the temperature of the camera, the more hot pixels. Under

bright lighting, hot pixel noise does not appear as frequently

and is not apparent because fewer pixels misre.

Common Methods for Reducing Hot PixelsThere are various tricks and techniques for reducing hot pixels

that range from Dark Frame Subtraction, discussed earlier, to

a number of methods that use pixel averaging and the Median

lter based on the consideration of pixel characteristics.

The latter two methods provide solutions in two very different

ways: calculating the average of a group of pixels versus

selecting the most representative pixel of a group of pixels.

If the averaging algorithm is applied to an entire image, theimage will appear blurred. Applying the Median lter, on the

other hand, will blur the image to a lesser degree, as straight

edges are not blurred.

When reducing a single hot pixel on a dark background, for

example, a pixel averaging approach raises the calculated

average of the pixel so that the luminosity between the color

of the hot pixel and the background is calculated and applied.

When pixel averaging is used on hot pixels, the corrected hot

pixels do not disappear; rather, they are simply blurred and areless distinguishable in context.

Other methods that rely on the Median lter utilize an

algorithm that focuses on a group of pixel blocks3x3

(9 pixels), 5x5 (25 pixels), or 7x7 (49 pixels) or more.

These methods select the one pixel from the cluster that is

most representative and replaces the hot pixel with the

representative one.

While the Median lter approach is often preferred over a pixel

averaging approach, both the Median lter and pixel averaging

often create plain and unnatural structures in the image. Even

more signicant, if the selected image contains any signicant

degree of random noise, a Median lter frequently introduces

unnatural structures in plain or low detail areas.

Although only one element

of a sensor misres, in

most cases, a hot pixel is

not a single pixel but maybe a cluster of as many as

12 pixels.



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The Threshold Dilemma

A variety of techniques for reducing or removing noise use

what is commonly known as a threshold. When a contrast

threshold is used to differentiate noise pixels from detail pixels,

the result is frequently an unnatural image change. When a

threshold is set in an image processing calculation, a threshold

level of detail is set and the specied calculations are applied

to detail that is either above or below that threshold. For this

reason, neither a hard or soft threshold should be used to

differentiate noise from detail.

While the use of a threshold setting can be effective to some

extent when used in the process of reducing the appearance of

noise, a hard threshold presents even greater problems from

a photographic perspective. When used in the noise reduction

process, some noise is distinguished from detail and some is not,

depending on the threshold setting and the corresponding size

of the detail. Detail that falls below the threshold is considered

and processed, and detail that is above the threshold is

left untouched. It is here that the problem often arises. The

effect of using a threshold is that the size of detail has no

relationship to the details photographic signicance. Small

detail may appear as noise, or it may appear as important

detail that signicantly contributes to the photographic natureof the digital image. At best, using a method that uses a hard

threshold to reduce noise is a compromise.

Reducing Noise While Sharpening

The use of a threshold is a common topic with regard to noise

reduction, especially when combined with image sharpening.

There are books full of techniques for noise reduction and

image sharpening, and many of these include methods that

employ the use of a threshold setting. Many of these noise

reduction methods often include an option to apply an Unsharp

Mask lter to the image as part of the overall noise reduction

process. The rationale for including this in the noise reduction

process is two fold. First, the objective is to bring back some of

the detail that is lost in the noise reduction process. Second,

this process seems to reduce noise by sharpening larger details

while leaving smaller details (including any residual noise)

unsharpened.



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This technique sounds completely rational and effective. In

limited cases with selective application, this method can be

useful, as larger details appear sharper and the smaller details,

including residual noise, are less apparent because they are not

sharpened.

However, the major problem created by this process, from a

photographic perspective, is obvious. Within this process, details

are ltered and considered, not based on their photographic

importance, but rather on their size relative to the hard

(or set) threshold. The result is that the image is sharpened

in a haphazard sense without regard to the importance of

specic details. The impact on the image can be seen more

clearly in print than on the screen, as some detail edges will

be sharpened and others not, regardless of their photographic

signicance.

The process of employing sharpening during the noise reduction

process also raises a signicant workow issue. Without specic

information related to the exact sharpening that was performed

on an image, sharpening is an enhancement that cannot be

undone or compensated for once it is applied. Without this

information and without a method for unsharpening the image,

it is not possible to remove the sharpening performed on an

image within the image editing process.

Most importantly, image sharpening should be performed after

the image editing process and just prior to the print process.

Applying any signicant degree of sharpening to the image in

the noise reduction process leaves the image far less susceptible

to an optimal image sharpening process, whether an image is

sharpened via an image editing application or within any RIP

(Raster Image Processor) process prior to output.

Using a threshold setting

uniformly relies on an

algorithm to alter details in

an image regardless of the

photographic signicance

of a specic detail.



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Technical Methodologies

Post-capture methods for noise reduction may use any one or

a combination of a number of detail modication processes

that range from blurring and softening pixel edges to altering

the color and/or luminosity of one or more pixels. A varietyof noise reduction methods are frequently used in software

applications, with many methods being combined in the process

of reducing noise.

Blur Variations

Pixel blurring variations are among the most common

methodologies for reducing noise. Various types of blurring

techniques can be used, many of which are implemented as

lters. These approaches often inspect a group of pixels around

a target pixel that is being processed, calculate an average for

that group of pixels (possibly weighing some pixels more than

others), and then apply a level of blurring. Blurring variations

can be effective in reducing the appearance of smaller noise,

but frequently it leaves important details blurred and out of

focus.

The most common blur variation is the Gaussian Blur lter,

which weights the group of pixels using the Gaussian bell curve.

Applying this bell-shaped image lter is similar to cropping

away the highest frequencies in a Fourier transformed image.

When applied, the effect is similar to an out-of-focus lter,

although it is different from a conventional out-of-focus effect

created in-camera.

There are two limitations to using the Gaussian Blur lter for

noise reduction. The rst and most well known limitation is

that when the Gaussian Blur is applied to an image globally,

it eradicates wanted details along with unwanted details.

Secondly, when the Gaussian Blur is applied without qualitative

input from the user, the detail softening that occurs is

difcult to integrate or combine into the original image while

maintaining a natural appearance. That is, there is no natural

way to objectively evaluate and mix a blurred effect (in

those areas were noise reduction is necessary) with the original

(those areas where no noise reduction is wanted) so that the

image looks natural.


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While a modied version of Gaussian Blur can be effective

when implemented in a fashion that considers photographic

characteristics of an image, a global application of the Gaussian

Blur suffers from the side effects of many noise reduction

processes.

Median Filter

The Median lter is also a common method for reducing noise

in a digital image. The Median lter inspects a number of pixels

(from 10 to 100) around the current pixel, then sorts that list

of pixels, selecting the middle pixel as the replacement pixel.

To some, the Median lter may appear to be identical to the

blurring method above. However, while the blurring effect

described above calculates the average of the inspected pixels,

the Median lter simply selects the pixel in the middle of the

sorted list. So if 100 pixels are considered with 51 black and 49

white pixels, the Median lter will select a black pixel, while

a blurring lter would return the average of those 100 pixels,

resulting in gray.

Typically, a Median lter inspects the pixels within a square of

a given radius. If applied with a radius of 5, for example, a list

of 121 values needs to be sorted for each pixel, often requiring

signicant processing power.

The Median lter also presents other potential issues when it is

used for global noise reduction purposes. Unlike the Gaussian

Blur, the Median lter is a non-linear lter that can alter the

image adversely when applied to details that are susceptible

to artifacting. Even when applied to a small degree, the

Median lter can introduce unpredictable, unnatural, and often

unwanted structures into certain detail types in an image.

Use of a Median lter can introduce painterly structures in an

image, which resemble small details in a watercolor painting,

or it may create faceting structures, which in some cases can

be even more distracting than the original noise. As a result,

the Median lter serves only a limited purpose as a technical

solution for selected noise reduction for specic detail types.

When applied globally,

the Median lter affects

a range of detail types

differently and can

introduce painterly

structures or faceting

artifacts to in an image.


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Fourier Transformation

Of the most commonly implemented methods for reducing

noise, the more complex approach is to Fourier Transform the

image into a space where the image is no longer represented

by pixels or vectors. Instead, the image is stored as frequencies

and then image corrections are made. When using the Fourier

Transformation, it is easier to locate certain structures in an

imagesuch as lines, edges, and patternssince noise does not

typically form such structures. A Fourier Transformation can be

used to distinguish details from noise in some cases; however,

as promising it sounds, this method is not the silver bullet for

noise reduction.

The problem is that this approach only works well in theory.

It can successfully differentiate between random noise andactual image details, especially details that are repeated or

are regular patterns. However, in actual digital images, the

frequency analysis method (the Fourier Transformation) too

often appears to interpret patterns as noise in areas that

actually dont contain any patterns. As a result, this approach

often leads to unwanted structures being introduced into the

image.


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Noise Management: Practical Considerations

19Effective Noise Reduction & Detail Optimization: An Analysis & Post Capture Processing Model

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In-Camera Noise Management

Image processing at the time of capture provides unique

advantages and challenges when addressing the issue of noise

in the digital image. An image is created as it is captured

and interpreted at the signal processing level (the imagecreation process within the camera). During the creation, the

manner in which the sensor (CCD or CMOS) creates the image

includes techniques to maximize detail while minimizing noise.

However, noise is, nevertheless, present in the image.

The challenges for reducing noise are faced equally in the

process of in-camera noise reduction. As you would expect,

detail and noise have the same close relationship in the capture

stage as when the image is captured and processed by the

camera. Among other considerations, the more signicantdangers from in-camera noise reduction are detail loss from the

noise reduction process and the danger of future loss of detail

(via repeated noise processing) in the post-capture image

editing stage.

Advantages

There are limited advantages to in-camera noise reduction.

In-camera noise reduction is done during an image-processing

phase within the camera that can adjust for specic variables

that affect noise. Variables such as CCD or CMOS temperature

and the ISO setting of the camera affect the presence of noise

and can be compensated for within the image-processing

phase. For example, if the CCD or CMOS temperature is T1 and

the ISO setting is I1, the camera can adjust the noise reduction

process based on known behaviors of the CCD or CMOS at T1

and at I1. If an image is captured again with the same CCD or

CMOS temperature of T1 but with a new ISO setting of I4, the

camera can adjust the noise reduction for that specic image

based on those variables and the known behaviors of the image

sensor (CCD or CMOS).

There are, however, disadvantages to utilizing noise reduction

within the camera, ranging from the lack of necessary

processing power within the camera for effective noise

reduction to the permanent loss of detail and the lack of

control over detail reduction.

There are disadvantages

to utilizing noise reduction

within the camera, ranging

from the permanent loss of

detail to the lack of control

over detail reduction.



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Issues & Challenges

The Permanent Nature of Noise Reduction

Loss of detail is among the most signicant side effects of in-

camera noise reduction, as noise reduction within the capture

device modies image detail. Just as with certain image

corrections, such as contrast and sharpening adjustments, noise

reduction is an image change that cannot be undone by the

user once applied to the image. These losses of detail occur

when algorithms in the camera remove image details that

it considers noise. This detail is lost even before the picture

is recorded to the cameras memory card. This discarding of

image detail is performed automatically and it is often based on

objective calculations that are not able to distinguish between

true photographic detail and noise.

Double Processing and Workflow

Among the challenges of in-camera noise reduction is

controlling its effect on image detail, especially its detrimental

effect on the image from a workow standpoint. Most

importantly, in-camera noise reduction limits the ability of the

user to optimize detail once noise reduction has been applied

inside the camera. Once the camera has made a determination

of what is noise and what is detail and then has processed

the image, options for the user to reduce noise are severely

limited, as the user faces the increased possibility of introducing

additional artifacts in the detail optimization process. Processing

the image a second time for noise will sacrice detail in the

post-capture process, causing the image to develop an even

more unnatural appearance.

The Need for Processing Power

Without question, being able to conveniently and immediately

reduce noise in-camera leads photographers and consumersto use in-camera noise reduction. However a good digital

camera capturing a 4 mega pixel image, for example, may

have only 1/5th of a second to process the image. In this time,

it is conceivable that roughly 50 operations per pixel can be

performed. While it may sound considerable, this is a limitation

that is signicant and restricts the effectiveness of noise

reduction that can take place within the camera. By contrast, a

Digital Raw & JPEG

Some high end digital cameras and

digital SLRs offer options to capture

and store processed les such as JPEGs

while also storing raw, unprocessedles. The benet of doing this is that

the user has an original le that can be

processed later based on the needs for

that image with regard to detail and

noise.



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state of the art computer can easily perform 1,000 times more

operations per pixel, enabling more advanced and dynamic

functionality in the noise reduction process. When combined

with other input variables such as the human eye, effective

noise reduction is better achieved in the post-capture stage.

Undesirable Effects of Reducing Noise

When applied improperly, noise reduction frequently creates a

range of undesirable effects in digital images. These byproducts

of noise reduction have a varying level of impact on an image,

ranging from the introduction of subtle and unnatural detail

changes to the creation of a completely articial-looking digital

image.

From a mathematical standpoint, many types of noise reductionartifacts are identical. However, from a subjective point of view

the various types of artifacting create aberrations that are often

signicant and apparent.

Blind Area Artifacting

Blind Area Artifacting occurs when high detail areas are noise-

reduced ineffectively based on assumptions about details within

a digital image. Detail in a digital image often ranges from

low to very high, depending on the image detail that is being

represented. When noise is effectively reduced in a low detail

area and then applied equally to an area which contains high

detail, important aspects of the image lose detail to the degree

that structure disappears or appears unnatural.

Blind Area Artifacting typically occurs in hair and other

ne detail structures. Hair structures and their details, for

example, while very subtle, are essential to the images natural

appearance. Changing those structures in the noise reduction

process calls unwarranted attention to that detail, resulting in

the unnatural feeling or perception of the image.

The unnatural impactof Blind Area Artifacting

occurs because the human

eye expects certain details

to be present in areas

that no longer have the

necessary detail, creating ablind area of detail.



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Detail, Noise Relationships, and Blind Area Artifacting

Blind Area Artifacting is a byproduct of many non-selective

noise reduction processes that are directly connected to the

relationship of noise-to-detail across a digital image.

In the accompanying illustration, the red line represents the

The mathematical explanation

for Blind Area Artifacting is

relatively straightforward.

Similar to the way that thehuman ear receives sound

logarithmically (exponential

increase of sound appears as a

linear increase of volume to the

listener), optical noise is often

perceived in a similar fashion.

This means, however, that if

there is an image structure in a

certain image area with contrast

that is 25% (one quarter) of the

noise in this area, the human eyewould perceive 100% noise and

50% (one half) existing image

structure, due to the logarithmic

nature of structure perception.

However, assuming that

structure and noise are

somewhat distributed with

Gaussian standard aberration,

the 100% noise and the 25%

image structure do not add

up to 125% structure in the

image. This calculation keeps

in mind that overlaying 100%

of a Gaussian noise structure

with the same amount (100%)

of another structure (be that

another noise source or actual

image detail) will typically

result not in 200% of structure

contrast, but only 141% image

contrast (square root of 2). Of

course, this may also vary based

on the characteristics of both

structures.

In short, when a noise structure

in an image area is perceived

by the human eye with only

slightly more contrast than the

existing image structure in the

same image area, the actual

image detail in this area is

slightly stronger than the detail

in an area where only noise

with no underlying structure

is present. In other words, thenoise swallows image detail

much faster than it appears to

the viewer. When the noise is

reduced, this important detail is

obscured, resulting in Blind Area

Artifacting and unnatural, often

plastic-like structures.

When noise in a structure is

perceived as having slightly

more contrast than the

surrounding context, the

aberrant detail tends to dene

the desirable detail to a degree

which is disproportionate to its

strength in the picture. And as

a result, in reducing the noise,

detail is sacriced.

Contrasting Noise and Detail



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noise intensity in an image (here assumed to be constant

throughout the image), and the blue line represents the

images detail structure, which will vary in different areas of

the image. The blue line goes from a relatively low point to a

relatively high point, as most images have varying degrees of

structural complexity.

At point 1 in the diagram, it can be seen that the detail

structure (blue) has approximately one third less contrast

than the noise level, while at point 2 the image structure is

only about one third stronger than the noise intensity. That

is, in one area within image, such as the sky of an outdoor

photo, noise is visually more dominant than the image details

themselves (represented by point 1 in the illustration). In these

cases, noise swallows the image detail and becomes the

dominant detail. However, in another area of the image, such

as an area consisting of foliage, the images detail structure is

more dominant than the noise (represented by point 2 in the

illustration).

When a non-selective noise reduction process attempts to

reduce these areas to the same degree or to similar extents,

image details that have a noise-to-detail relationship as

indicated in point 1 become the focus of the noise reduction

process and often erase or diminish these details. The result

is the creation of Blind Areas within image details, and since

these details have an important relationship in the image from

a photographic perspective, the noise reduction process creates

problems rather than solves them.

Remaindered Pixels

Remaindered Pixels often appear as single, aberrant pixels that

for one reason or another were not considered by the noise

reduction process, leaving a single noise pixel among dissimilar

detail. Remaindered Pixels are often apparent in areas with lowor plain detail after certain noise reduction routines are used.

Remaindered Pixels can occur as byproducts of a number of

noise reduction methods. They frequently appear as the result

of simplistic noise reduction methods or those that consider and

lter details based on a threshold approach.

The Need For Selectivity

While in some instances noise can be

readily differentiated from detail (high

frequency noise against soft structures

such as clouds, for example) most noise

occurs in the presence of detail in such

a way that selective noise reduction

approaches are necessary to obtain the

desired results.

Deta

ilStr

uctu

re

Noise Intensity



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If, for example, an image area that has 1,000 pixels with a

considerable dispersion of noise, there will typically be a small

percentage of pixels out of this 1,000 with noise strength or

intensity relative to the surrounding pixels that is much higher

than the average noise strength. Many conventional noise

reduction algorithms, typically those that utilize threshold

methods, leave a few pixels in the image unaffected, as the

algorithm assumes that they are relevant details based on a

xed threshold.

The result is randomly remaindered and distributed pixels in

the image that are not considered to be noise, regardless of

their impact on the image. This small number of pixels with the

highest noise intensity in an image will remain unaffected and

can become even more apparent against the low detail areas.

This Remaindered Pixel effect is often most apparent with

digital cameras that use built-in noise reduction. In general,

built-in noise reduction techniques in digital cameras produce

the most artifacts, since the processing capabilities of digital

camera chips are very limited when compared to computers,

and camera chips are not sufcient for processing sophisticated

algorithms.

Remaindered Pixels also occur typically when noise is not

sufciently reduced or when the noise reduction algorithm isnot capable of considering a large enough sample of the image

information. Noise reduction methods that can only compare

a pixel with its directly adjacent pixels (such as most noise

reduction methods inside digital cameras) are less effective in

distinguishing wanted contrast from unwanted contrast, further

contributing to the creation of Remaindered Pixels.

Painterly Effect

The Painterly Effect is a byproduct of noise reduction where

certain details are obscured to a point that it appears similar

to a painted detail. Painterly-looking details appear when

noise reduction methods are used that change the structure of

the image as they perform more complex operations, such as

reducing any pixels contrast by a certain extent.

Typically, more complex noise reduction methods, such as

a Median lter, change the images structure to a point

When a noise reduction method takes only

a small sample from the image, it can take

only few pixels into account, such as 3x3 or5x5 pixel block. These methods frequently

cannot make signicant or profound

considerations whether one pixel is part of

an unwanted noise structure or not. Given a

small block of pixels, these noise reduction

calculations are no more capable than the

human eye of determining whether the

outlined pixel above, for example, is noise

or important detail.

When a noise reduction method uses a

larger block of pixels, as shown above,

more intelligent considerations about the

pixel are possible and the algorithm is not

limited to more simplistic approaches as

those that rely on a threshold concept.



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where the image may not appear in certain areas to be noise

reduced; however, in the process, ne details become altered

to a point where unnatural structures appear.

The Painterly Effect becomes a signicant problem because this

type of artifacting appears to the human eye to be among themost unnatural type of structure. When more complex noise

reduction methods attempt to reconstruct edges or displace

pixels (such as the Median lter) painterly artifacts become

more dominant in higher detail areas. Painterly-looking

artifacting can be found commonly in offset printed images

where a Median lter or a similar method is used to reduce

noise in detailed areas.

Blurring Effect

A Blurring Effect is arguably one of the more common side

effects of noise reduction. When blurring methods are used

as a primary basis for noise reduction, image details become

softened to the same degree as the targeted noise. While all

noise reduction methods reduce image detail to some extent,

the Blurring Effect appears as an unnecessary softening of the

image in the noise reduction process.

The Blurring Effect frequently appears in a noise-reduced

image because of the complex relationship between noise and

image detail and the varying degrees of noise that appear from

image to image. Blurring noise by a factor of X in one image,

for example, will not have the same qualitative impact when

applied to the same degree in another image. When noise

reduction methods use blurring methods to globally reduce

noise, an unnecessary Blurring Effect can result in varying

intensities, depending on the image and its details.

Resolution Issues: Screen Versus Print Images

Print Optimized Versus Screen View PresentationOptimized noise reduction for print must be considered

differently from optimized noise reduction for on-screen

viewing. As the effective resolution in print is often three to

four times as high as the resolution at which an image appears

on the screen, details must be dealt with differently in each

scenario. This is not to say that a print optimized image

cannot be displayed on a CRT, LCD, or plasma display screen.

When noise reduction

methods use blurring

methods to globally reducenoise, an unnecessary

Blurring Effect can result

in varying intensities,

depending on the image

and its details.



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However, as both image presentation methods present image

details, the nal method that will be used for the presentation

of the detail becomes a primary consideration in the noise

reduction process.

Print optimized noise reduction must consider and respect thenature of the noise as detail, since noise is integral to image

detail. The objective is to reduce its appearance to a level

below the threshold of ordinary perception. In our analysis of

noise reduction and detail optimization, we emphasize the nal

presentation of the image as the standard for judging noise,

rather than the noise reduction process itself. In other words, it

is not the algorithm or mathematical process and its ability to

reduce noise that is important; rather, it is the nal condition of

the image in print across a variety of images that we consider.

In doing so, it is the users perception of the detail in the image

that determines the effectiveness of the entire noise reduction

process.

As we consider this, we acknowledge that there are different

needs for noise reduction and detail optimization for screen

viewed images than for the printed image. Because the image

is presented on paper, the effective resolution is greater than it

would be for the same image viewed on a screen. Therefore, it

is important to process the image based on the printed image.

Effective noise reduction should consider the print process,

the manner in which details appear in print, and the higher

resolution that the print process utilizes. By optimizing noise

reduction for print, image detail is considered for the higher

of the two presentation standards, enabling the image to be

effectively optimized and presented via either medium.

The Non-Scientic and Subjective Nature of Perception

Regardless of how good the camera is or how mathematically

perfect the post-capture processing, the creation of a qualityimage depends on a good eye and good subjective judgment.

An optimized system for noise reduction needs to consider the

subjective nature of perception among other dynamic factors in

the noise reduction process.

Visible noise detracts from the photographic qualities of

the image at one level, while noise that appears below the

level of perception detracts at a different level and must be

Screen and Print Resolutions

Resolution and detail presented via a

computer monitor appears only at a

fraction of that of the printed image.

Effective monitor resolution can beapproximated by calculating the

following:

The approximated display resolution of

a 19 inch monitor with a video card/

adapter setting of 1024x768 can be

calculated by dividing the HORIZONTAL

resolution of the video card setting by

the physical HORIZONTAL measurement

of the screen 1024/14.5 = 70.62 dpi.

By optimizing noise

reduction for print, image

detail is considered for

the higher of the two

presentation standards,

enabling the image to

be effectively optimized

and presented via either

medium.



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dealt with differently. This difference needs to be identied

and recognized in developing an effective noise reduction

system from a qualitative (perceptual) and mathematical

(quantitative) perspective. Again, we stress that the manner in

which noise is manifested in the image (how the noise appears)

is a key variable and a fundamental element of effective noise

reduction.

Details and the Power of the Human Eye

Practical & Subjective Issues in Noise Reduction

Photographic detail and the perception of detail in the image

are also key considerations for optimizing detail naturally.

Various noise reduction methods approach noise globally,

providing the user with variables to adjust, resulting in a

range of results. As we discuss in the preceding section, these

methodologies often suffer from a range of side effects that

occur as a result of the dynamic nature of the image and image

detail as well as the limitation of any individual method. It is

important to recognize that there are dynamic aspects of the

digital photograph that cannot be addressed purely by one

single algorithm or method for all images.

Detail structures appear differently from detail type to detail

type. Skin, for example, appears differently than foliage,

and both foliage and skin appear differently than sky detail.

Additionally, image detail appears differently when in proximity

to different colors. These differences are among other

signicant considerations in the noise reduction process, as the

objective is to control the perceptual level of noise reduction

and ensure detail optimization.

An optimal system for detail optimization and noise reduction

involves varying mathematical approaches and varying degrees

of interaction that include these detail and color considerations

while also factoring in the human eye and the tools of imaging

science.

There are occasions when a process can be created to provide

visually acceptable noise reduction in an automated fashion

when appropriate variables and specic image detail are

considered. In these cases, tools can be created to adapt to the

specic needs of the capture process and the images that will

How noise appears in an

image is a key variable and

a fundamental element of

effective noise reduction.



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be captured. But because noise and details differ from image

to image, an optimal approach requires the human eye to

distinguish noise from detail and to distinguish what appears to

be natural.

The challenge of noise reduction is to provide an effectivemethod that can adapt to the dynamics of the image, the

specic details of the image, and ultimately the manner in

which the human eye perceives the noise-to-detail relationship.



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Power, versatility, and control together contribute to an

effective noise reduction and detail optimization solution. To

be effective, a noise reduction system must work in conjunction

with all aspects of the digital image.

However, as powerful as a solution may be, in order to be

effective it must be simple to use and it must empower the

user to be effective in the image editing process. When we

consider the constant changes and advances in digital imaging

technologies, users are left with the daunting task of learning

and staying current with technology.

An effective noise reduction and detail optimization process

provides users with a progressive tool that offers ease-of-

use and power while considering users varying levels of

sophistication. A progressive noise reduction tool offers a

process that enables users to adapt the process to their needs

and empowers them to be effective at their level, while

being able to expand their abilities as they grow with digital

photography and adapt to the latest technologies that are

available.

Dne provides an effective system for noise reduction and

detail optimization through a series of tools that are as

powerful or as simple as the user wants it to be.

Dne provides a system that enables the user to:

1. Preview and analyze the image

2. Optimize an image based on a specic digital camera

3. Reduce random color noise and maintain color details

4. Optimize image details and their relationship to artifacts

5. Selectively reduce noise quickly and intuitively

6. Optimize color changes and their relationship to detail

7. Control light and contrast in image details

Regardless of how noise is manifested in the image, Dne

provides a system for reducing noise with respect to image

detail. Dne utilizes a number of tools that provide options for

reducing noise based on the appearance of noise in the image,

An effective noise

reduction and detail

optimization process

provides users with aprogressive tool that offers

ease-of-use and power

while considering the users

level of sophistication.



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ranging from camera-specic proles to selective application

tools that reduce noise based on the presence of specic detail

types.

1. Previewing and Analyzing the Image

It is important for the user to be able to preview, analyze, and

observe the process of noise reduction and detail optimization.

Viewing details and their relationship to noise is an essential

part of an efcient post-capture noise reduction process.

However, identifying and detecting noise can be one of the

more difcult steps in noise reduction. Lower screen resolutions

(compared to print) and varying types of image detail can

make it difcult to identify and reduce noise while maintaining

the natural photographic appearance of the image.

Auto-Detection of Noise in Sensitive Areas

The Auto-Detection feature in Dne acts as a key tool for

identifying noise. When Dne is opened and the ve-preview

window option is selected, the Auto-Detection feature locates

three areas in the image that contain characteristics that are

typically subject to noise.

The Auto-Detection feature enables users to locate and observe

the noise reduction process in sensitive areas, or to locate other

areas with these characteristics and selectively reduce noise inthe image. The Auto-Detection system is designed to help the

user identify noise in the image, thereby serving as a tool for

analysis as well as helping to educate users by identifying areas

that are susceptible to noise in an image.

Multi-Preview Mode

The multiple preview option in Dne is an important tool

in optimizing detail and treating noise in the image. Most

conventional analysis tools provide zooming previews of a

single area of an image in order to view the changes to the

image in the noise reduction process. Moving zoom options are

also popular methods for analysis of an image in the editing

process.

However, as detail is changed in one characteristic or image

feature, other details are often affected. Observing multiple

details and image characteristics in Dne enables the user to

Preview #1 Locates an area with extensive

highlights. Preview #2 locates an area with

a higher degree of edge detail. Preview #3

locates an area with shadows or low light.

Preview windows #4 and #5 enable before

and after views of the image during the

editing process.

The Dne Preview System



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make adjustments to focal or sensitive areas of the image while

observing their effects on other details.

Analysis Mode: Screen-Based Image Analysis Tools

Analyzing noise and detail in an image on screen is one of

the limitations that makes post-capture noise reduction more

difcult. When reducing noise for a print process, we discuss

the fact that computer screen resolutions and the differences

between display resolution and print resolution are often

impediments to optimizing detail while reducing noise. A

preview zoom feature alone is often insufcient to accurately

and efciently analyze image detail.

The preview analysis tool within Dne plays an important

role within the detail optimization process. Details with specic

characteristics, sensitive areas, and their relationship to the

image as a whole can be viewed to ensure that detail is

maintained and the appropriate amount of noise reduction is

performed.

Grab and Drop Preview

The Grab and Drop preview feature is an important tool

within Dne that makes it easy to perform a balanced image

enhancement or adjustment. The Grab and Drop preview tool

is an efcient image analysis system that enables the userto grab a detail from any preview window and drag it to an

adjacent window. By grabbing and dropping image details, the

user can easily compare sensitive detailin either the normal

or the analysis modeand view the image before and after the

noise removal process.

2. Optimizing Images Based on a Specic Camera

The Unique Nature of Digital Cameras & Image Details

As discussed earlier, all digital cameras capture an image inthe same way, but utilize different calculations to process the

image. That is, each cameras image sensor assembles and

processes the information it captures differently. Aside from

the functional features of the camera, the manner in which

cameras process captured data is the main differentiator from

camera to camera, which necessitates varying noise reduction

approaches for different cameras and a exible tool for

Above: Specic details in the face are

grabbed and dropped from preview #4

(lower left) up to the preview #2 (top

middle). The preview is adjusted to a 100%(1:1) view to observe changes at actual

image size.

Above: The Analysis Mode enables users

to identify details and observe luminosity

changes to avoid blowing out high contrast

details or dropping off low contrast

details.



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dynamic noise reduction in the captured image.

The Detail-to-Color Correlation

Targeted Reduction and the Camera Profile Controller

As previously discussed, noise appears in digital images andbecomes intertwined among varying types and varying levels

of image details. Additionally, noise becomes apparent when

present against different colors and detail structures in a digital

image.

Common solutions for noise reduction typically involve isolating

an individual color channel of an image. The occurrence of

noise as it appears in the different color channels of a digital

image is often discussed when the issue of noise reduction is

addressed. The presence of noise in the blue channel of anRGB image, for example, often leads many users to focus on

the reduction of noise in one particular color channel without

regard to other elements of the image.

When noise is reduced in one channel of an image, the

contrast of edges or the shape of details in only one channel

of the image is changed without regard for the relationship

of the two remaining color channels and their independent or

combined effect on the image. The practice of single-channel

noise reduction treating one third of an image componentwithout regard to related components is utilized in no other

photographic, optical, or similar medium or technology because

the results are not true to the original image.

The more effective implementation of targeting noise is

provided in Dne and involves a proprietary process for

considering detail and color in the noise reduction process.

This system not only allows the user to target noise within a

problem area, but also provides a system of calculations to

balance the photographic relationship of detail in the image.

This becomes important for a variety of reasons. Most

importantly, at the conscious level, we can reduce noise

effectively using the relationship of color and noise at the pixel

level. Rather than solely isolating the color channel, we are

able to identify specic colors (such as the blue color in a sky

scene), their relationship to noise, and their relationship to

Targeting the blue channel

of an RGB image ignores

some of the basic tenets of

photographic relationshipsin the digital image.



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detail across the image. Below the level of perception, noise

plays an important role in the detail structure of the image.

As the user identies colors that need to be treated with noise

reduction, detail structures and their relationship to these

structures can be considered across the image.

To obtain a natural reduction of noise while optimizing wanted

detail, the user is able to identify the targeted area for

reduction while setting the detail-sensitive area to have a low

level of reduction. A balancing of noise and detail will occur

based on the presence of detail and its relationship to color

across the image. As certain colors present noise differently,

and as the human eye discerns colors differently (not all colors

are perceived to the same degree), the image benets from

being treated in a dynamic fashion to achieve a more natural

result. Limitations of noise reduction in specic detail areas can

be identied to provide additional control over the image.

3. Reducing Color Noise and Maintaining Color Details

The reduction of Chrominance Noise (color noise) is frequently

discussed in books, seminars, and classes related to image

editing. The random nature of Chrominance Noise often makes

dealing with this type of noise difcult. Among the difculties is

the need to maintain color details and their transitions to avoid

contributing to a digitally processed appearance. When randomChrominance Noise is reduced inappropriately, it can create

an unnatural appearance in color details. When inappropriate

Chrominance Noise reduction is combined with other similar

image changes, the effect can have an even more negative

impact on the image.

Blurring Lab Channels Versus Dfine Detail Protection

Converting an image to Lab color mode and blurring the

a and b color channel information is a frequently taught

method for Chrominance Noise reduction. However, the impact

of using this technique can vary signicantly across images and

have a negative impact on the image. Similar to blurring the

blue channel of an RGB image, Lab color channel blurring uses

an approach that is utilized in no other photographic, optical,

or similar medium or technology because the results are not

true to the original image.



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[Figure 1]

The color circles in this original illustration show

a distribution of how random chrominance noiseappears against colors in an image.

[Figure 2]

When a and b channel blurring is performed

on an Lab color mode image, image detail is

blurred and color transitions can suffer from a

haloing effect as detail is lost on the color edge

transitions.

[Figure 1a]

An illustration of the distribution of luminosity

(the brightness) of noise in Figure 1. The smallwaves on this luminosity distribution curve

represent noise, with the large transition in

the curve representing a color transition at the

edge of a color circle in Figure 1.

[Figure 2a]Articial color transitions occur when

important image detail is blurred. This global

approach to addressing random noise lacks

any consideration for the image details and

can create an articial appearance in images,

which will vary from image-to-image.



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When color detail is blurred (within the a and b channels) with

the objective of reducing noise, the transition that is created

is softer and edge detail is lost, as shown in Figure 2. When

blurring Lab color channels, the image will contain softer color

transitions in the color channels than in the luminance channel.The result of this blurring can have a variety of impacts on the

image.

When reducing chrominance noise, the protection of color

detail becomes one of the primary considerations. When

Chrominance Noise reduction considers image details,

important color transitions can be controlled and maintained

in the noise reduction process to create unmodied color

transitions, as illustrate

Date post:	07-Apr-2018
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Noise Reduction Whitepaper

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