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Lecture 1

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Image Processing

Eng. Ahmed H. Abo absa

E-mail: a.absa@up.edu.ps

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Lecture 1

What is Digital Image Processing

Processing digital images by means of a digital computer.

A digital image can be modeled as a two dimensional function , ,where x and y are spatial coordinates, and the value of the function is the intensity or gray level of the image at that point.

),( yxf

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Lecture 1

What is Digital Image Processing

A digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, pixels, and pels.

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Lecture 1

Digital Image Processing

Image Enhancement

Image Restoration

Image Understanding (or Computer Vision)

Image Coding (or Image Data Compression)

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Lecture 1

Image Enhancement

Goal to accentuate certain image features for subsequent

analysis or for image display

Input : image Output : image

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Lecture 1

Image Enhancement

Techniques Contrast enhancement histogram equalization pseudo coloring noise filtering edge sharpening smoothing

Applications processing of remote-sensed image via satellite radar, SAR, Ultrasonic image processing

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Lecture 1

Image Restoration

Goal to remove or minimize known/unknown degradations in

image

Input : image Output : image

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Lecture 1

Image Restoration

Techniques De-blurring noise filtering correction of geometric distortion inverse filtering Least mean square(Wiener) filtering

Applications remote-sensed image processing noise cancellation

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Lecture 1

Image Understanding

Goal to interpret or describe the meaning contained in the

imageInput : image Output : interpretation(description)

““ME”ME”

““circle”circle”

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Lecture 1

Image Understanding

Techniques boundary descriptor regional descriptor relational descriptor

Applications character recognition automatic inspection of industrial parts ATR(automatic target recognition) target tracking

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Lecture 1

Image Data Compression

Goal to reduce the amount of data required to represent

images

Input : image Output : bit-stream data

“010100101100110101001 . . . .”

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Lecture 1

Image Data Compression

Techniques Error-free coding( or lossless coding) Lossy compression Image Compression Standard

JPEG, H.261, H.263, MPEG-1,2,4 etc

Applications Transmission

teleconferencing ,TV system, remote sensing via satellite Storage

VOD(video on demand), Video CD, DVD(digital video disk), medical imaging, educational and business documents

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Lecture 1

wavelength (Angstroms)

cosmic rays

gamma rays

X-Rays UV

visible

IR

Electromagnetic Spectrum

1 Å = 1 0 - 1 0

m

- 4 - 2 2 4 6 8 1 0 1 2 1

microwave (SAR)

radio frequency

10 10 10 10 10 10 10 10

The whole electromagnetic spectrum is used by “imagers”

Imaging

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Lecture 1

From the gigantic…

The Great Wall

(of galaxies)

1 0 2 8

m

Scales of Imaging

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Lecture 1

video camera

1 m

… to the everyday …

Scales of Imaging

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Lecture 1

… to the tiny.

1 0 - 6 m

electron microscope

Scales of Imaging

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Lecture 1

Digital Image Formation

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Lecture 1

Matrix Representation

H=256

W=256

Divide into 8x8 blocks

169130

173129

170181

170183

179181

182180

179180

179179169132

171130

169183

164182

179180

176179

180179

178178167131

167131

165179

170179

177179

182171

177177

168179169130

165132

166187

163194

176116

15394

153183

160183

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Lecture 1

Image Resolution

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Lecture 1

Image Resolution

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Lecture 1

Images and videos are multi-dimensional (≥ 2 dimensions) signals.

2-D image

Dimension 1

Dimension 2

Dimension 1

Dimension 2

Dimension 3

3-D Image Sequence or video

Dimensionality of Digital Images

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Lecture 1

The Human Visual System (HVS)

LG N

prim ary visua lcortex

h igher leve l vis ionand cogn ition

righ t eyele ft eye

re tina

lens

cornea

fovea

optic nerve

pup il

visua l axis

re tina re tina

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Lecture 1

HVS: Foveated Vision

Foveated vision: non-uniform resolution of the visual field, highest at the point of fixation and decreasing rapidly

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Lecture 1

HVS: Visual Illusion

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Lecture 1

Find the black dot

HVS: Visual Illusion

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Lecture 1

What is this?

HVS: Visual Illusion

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Lecture 1

Which lines are straight?

HVS: Visual Illusion

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Lecture 1

Color

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Color: RGB Cube

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Color: RGB Representation

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Lecture 1

Where Are We?

Imaging? Computer Vision?

Display/Printing?

Digital ImageProcessing

Computer Graphics?

BiologicalVision?

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Lecture 1

What Do We Do?

Image Processing/Manipulation

Image Coding/ Communication

Image Analysis/Interpretation

Digital ImageProcessing

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Lecture 1

Image Processing: Image Enhancement

Enhance

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Image Processing: Image Denoising

Denoise

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Image Processing: Image Deblurring

Deblur

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Image Processing: Image Inpainting

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Image Processing: Image Stylization

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Image Analysis: Edge Detection

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Image Analysis: Face Detection

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Image Analysis: Image Segmentation

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Lecture 1

Two deceivingly similar fingerprints of two different people

Image Analysis: Image Matching

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Lecture 1

Image Coding: Image Compression

compressed bitstream

00111000001001101…

(2428 Bytes)

imageencoder

imagedecoder

original image

262144 Bytes

compression ratio (CR) = 108:1

From [Gonzalez &

Woods]

From [Gonzalez &

Woods]

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Lecture 1

• Lossless image compression– Information preserving

original image can be exactly recovered– Low compression ratio– JPEG-LS, JBIG …

• Lossy image compression– Lose information

original image can be recovered, but not the same

– High compression ratio– JPEG, JPEG2000 …

Image Coding: Image Compression

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Lecture 1

JPEG (CR=64) JPEG2000 (CR=64)

discrete cosine transform based wavelet transform based

Image Coding: From JPEG to JPEG 2000

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Lecture 1

Image Coding: Video Compression

• From static images and image sequences (video)– From 2D to 3D– Strong correlations between frames– Representing motion

• Video compression– Compress each frame independently – Motion-compensated video compression

high compression ratio– MPEG1, MPEG2, MPEG4, H.264 …

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Lecture 1

Image Quality/Distortion Measures

_ =

M

i

N

jijij yx

MNMAE

1 1

1

Y || X_ = Z

ijx| |ijy ijz_ =For each pixel:

Mean Absolute Error (MAE):

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Lecture 1

Image Quality/Distortion Measures

M

i

N

jijij yx

MNMSE

1 1

21Mean Squared Error

(MSE):

Peak Signal-to-Noise Ratio (PSNR) in decibel (dB):

M

i

N

jijij yx

MN

L

MSE

LPSNR

1 1

2

2

10

2

10 1log10log10

L: Dynamic range of pixel intensityL = 2B – 1, where B is the number of bits to represent a pixelExamples:

8bits/pixel gray-scale image L = 25512bits/pixel gray-scale image L = 4095

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Lecture 1

Image Quality/Distortion Measures

original

MAE = 0MSE = 0

PSNR = infinity

noisy image 1

MAE = 7.99MSE = 100

PSNR = 28.1dB

noisy image 2

MAE = 15.9MSE = 394

PSNR = 22.2dB

noisy image 3

MAE = 38.2MSE = 2250

PSNR = 14.6dB

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Lecture 1

Image Quality/Distortion Measures

_ =

9375.1211690430100120144

1

MAE

Y || X _ = Z

ijx| |ijy ijz_ =

1

3

8

6

6

8

6

11

8

10

8

9

9

7

10

10

2

3

8

6

8

8

7

12

5

9

4

15

9

9

1

11

1

0

0

0

2

0

1

1

3

1

4

6

0

2

9

1

• Example: two 4 x 4, 4bits/pixel image

6875.9411368101690100140144

1

MSE

dBMSE

PSNRB

7.136875.9

15log10

)12(log10

2

10

2

10