Image Enhancement

vibhor kumar

Hannu Laaksonen

course: biomedical image processing

Biomedical Image Analysis

Rangaraj M. Rangayyan

Topics to be covered

1) Convolution mask Operations

. unsharp masking

. Sobtracting Laplacian

2) High Frequency Emphasis

3) Homomorphic filtering for Enhancement

4) Adaptive Contrast enhancement

Convolution Mask Operators - Unsharp masking

The Generalized equation of unsharp masking is

f(m,n) = [g(m,n) - µg(m,n)] + g(m,n)

Is calculated as average of the pixels in the window taken around the pixel(m,n)

blurred image

The weight can be changed according to desired effect.

For e.g. For a 3X3 convolution mask the unsharp masking is given by

- 18

- 18

- 18

- 18

- 18

- 18

- 18

- 18

2

Subtracting Laplacian

The degraded image may be expressed in a Taylor series as

gtg

t2

2g22

g(x,y,) = g(x,y,0) + (x,y,t) - + …...

Taking = k2g using

diffusion model

gtg

We get fe = g - k2g

For k = 1 and mask 3X3 we get subtracting Laplacian as: 0 -1 0

-1 5 -1

0 -1 0

Unlike Laplacian subtracting Laplacian maintain the intensity information while making the image sharp

(a)

(d)

(b)

(c)

(a) original lena image

(b) Laplacian

(c) Unsharp masking

(d) subtracting laplacian

High-frequency Emphasis

Highpass filtering are useful in detecting edges but for enhancing the images it is necessary to maintain the intensity information.

High-emphasis filter does the image enhance keeping the intensity information.

The Butterworth high-emphasis filter can termed as:

2

D0

D(u,v)2n

1 + (sqrt(2)-1 )1 +H(u,v) =

Filter gain

Frequency

(a) Original shape image

(b) the ideal high pass filter

(c) The Butter worth highpass filter

(d) the Butterworth high-emphasis filter

Enhancement using Homomorphic filtering

transformlinear filtering and enhancement

inverse transform

input image filtered

image

(a) Original Image

(b) log transform of original image

(c) Homomorphic filtering including a Butterworth high-emphasis filter

(d) Butterworth high imphasis filter only

Graphical Models and Image Processing, 54(3):259-267,May 1992

Adaptive Contrast Enhancement

Adaptive-neighborhood contrast enhancement:(1) non overlapping regions segmentation

(2) Overlapping regions segmentation

seed fill region growing:The region consists of spatially connected pixels that falls that fall within the specified gray level devaiation from seed pixel.

every time data is devided into back ground and foreground pixels

The growth tolerance threshold is highly important factor

Adaptive Contrast Enhancement

contrast enhancment can be done using the formula

fe = b (1+Ce)/ (1-Ce)mean background value

increased contrast

(a) Part of mammogram with a cluster of calcification

(b) adaptive-neighborhood contrast enhancement

(c) gamma correction

(d) unsharp masking

IEEE Transcation on Medical imaging 11(3):392-406,1992

Topics covered

1) Convolution mask Operations

. unsharp masking

. Sobtracting Laplacian

2) High Frequency Emphasis

3) Homomorphic filtering for Enhancement

4) Adaptive Contrast enhancement