Digital Image Processing (Lab 07)
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This document summarizes a presentation on digital image processing techniques. It discusses contrast-limited adaptive histogram equalization (CLAHE), histogram equalization, image intensity adjustment, adding noise, and median filtering. Methods like adapthisteq, histeq, imadjust, imnoise and medfilt2 are demonstrated to enhance contrast, adjust values, add noise and reduce salt and pepper noise through median filtering. Examples provided apply these techniques and compare results.
![Examples
• Apply Contrast-limited Adaptive Histogram Equalization (CLAHE) to an
image and display the results.
I = imread('tire.tif');
A = adapthisteq(I,'clipLimit',0.02,'Distribution','rayleigh');
figure, imshow(I);
figure, imshow(A);
• Apply CLAHE to a color image.
[X MAP] = imread('shadow.tif'); % Convert indexed image to true-color (RGB) format
RGB = ind2rgb(X,MAP); % Convert image to L*a*b* color space
cform2lab = makecform('srgb2lab'); %Create color transformation structure
LAB = applycform(RGB, cform2lab); % Scale values to range from 0 to 1
L = LAB(:,:,1)/100; % Perform CLAHE
LAB(:,:,1) = adapthisteq(L,'NumTiles',...
[8 8],'ClipLimit',0.005)*100; % Convert back to RGB color space
cform2srgb = makecform('lab2srgb');
J = applycform(LAB, cform2srgb); % Display the results
figure, imshow(RGB);
figure, imshow(J);](https://image.slidesharecdn.com/lab07-160824022532/85/Digital-Image-Processing-Lab-07-7-320.jpg)
![histeq
• Enhance contrast using histogram equalization
Syntax
J = histeq(I, hgram)
J = histeq(I, n)
[J, T] = histeq(I,...)
newmap = histeq(X, map, hgram)
newmap = histeq(X, map)
[newmap, T] = histeq(X,...)
Description
• histeq enhances the contrast of images by transforming the values in
an intensity image, or the values in the colormap of an indexed
image, so that the histogram of the output image approximately
matches a specified histogram.](https://image.slidesharecdn.com/lab07-160824022532/85/Digital-Image-Processing-Lab-07-8-320.jpg)
![imadjust
• Adjust image intensity values or colormap
Syntax
J = imadjust(I)
J = imadjust(I,[low_in; high_in],[low_out; high_out])
J = imadjust(I,[low_in; high_in],[low_out; high_out],gamma)
newmap = imadjust(map,[low_in; high_in],[low_out;
high_out],gamma)
RGB2 = imadjust(RGB1,...)](https://image.slidesharecdn.com/lab07-160824022532/85/Digital-Image-Processing-Lab-07-10-320.jpg)
![Examples
• Adjust a low-contrast grayscale image.
I = imread('pout.tif');
J = imadjust(I);
imshow(I), figure, imshow(J)
• Adjust the grayscale image, specifying the contrast limits.
K = imadjust(I,[0.3 0.7],[]);
figure, imshow(K)
• Adjust an RGB image.
RGB1 = imread('football.jpg');
RGB2 = imadjust(RGB1,[.2 .3 0; .6 .7 1],[]);
imshow(RGB1), figure, imshow(RGB2)](https://image.slidesharecdn.com/lab07-160824022532/85/Digital-Image-Processing-Lab-07-11-320.jpg)
![medfilt2
• 2-D median filtering
Syntax
B = medfilt2(A, [m n])
B = medfilt2(A)
B = medfilt2(A, 'indexed', ...)
B = medfilt2(..., padopt)
Description
Median filtering is a nonlinear operation often used in image
processing to reduce "salt and pepper" noise. A median filter is
more effective than convolution when the goal is to
simultaneously reduce noise and preserve edges.](https://image.slidesharecdn.com/lab07-160824022532/85/Digital-Image-Processing-Lab-07-14-320.jpg)
Digital Image Processing (Lab 07)
- 1. Presented by: Dr. Moe Moe Myint Information Technology Department Technological University (Kyaukse), Myanmar Digital Image Processing moemoemyint@moemyanmar.ml www.slideshare.net/MoeMoeMyint
- 2. • Only Original Owner has full rights reserved for copied images. • This PPT is only for fair academic use.
- 3. Image Enhancement (Lab 7) M. M. Myint Dr. Moe Moe Myint Information Technology Department Technological University (Kyaukse) - To process an image so that the result is more suitable than the original image for a specific application
- 4. Objectives • To apply contrast-limited Adaptive Histogram Equalization (CLAHE) to an image • To enhance contrast using histogram equalization • To adjust image intensity values or colormap • To add noise to image and Display the results Required Equipment • Computers with MATLAB software and Projector Practical Procedures • Use the adapthisteq command • Use the histeq command • Use the imadjust command • Use the imnoise command • Use the medfilt2 command
- 5. Image Enhancement • adapthisteq Contrast-limited adaptive histogram equalization (CLAHE) • histeq Enhance contrast using histogram equalization • imadjust Adjust image intensity values or colormap • imnoise Convert integer values using lookup table • medfilt2 2-D median filtering
- 6. adapthisteq • Contrast-limited adaptive histogram equalization (CLAHE) Syntax J = adapthisteq(I) J = adapthisteq(I,param1,val1,param2,val2...) Description • J = adapthisteq(I) enhances the contrast of the grayscale image I by transforming the values using contrast-limited adaptive histogram equalization (CLAHE). • J = adapthisteq(I,param1,val1,param2,val2...) specifies any of the additional parameter/value pairs listed in the following table. Parameter names can be abbreviated, and case does not matter.
- 7. Examples • Apply Contrast-limited Adaptive Histogram Equalization (CLAHE) to an image and display the results. I = imread('tire.tif'); A = adapthisteq(I,'clipLimit',0.02,'Distribution','rayleigh'); figure, imshow(I); figure, imshow(A); • Apply CLAHE to a color image. [X MAP] = imread('shadow.tif'); % Convert indexed image to true-color (RGB) format RGB = ind2rgb(X,MAP); % Convert image to L*a*b* color space cform2lab = makecform('srgb2lab'); %Create color transformation structure LAB = applycform(RGB, cform2lab); % Scale values to range from 0 to 1 L = LAB(:,:,1)/100; % Perform CLAHE LAB(:,:,1) = adapthisteq(L,'NumTiles',... [8 8],'ClipLimit',0.005)*100; % Convert back to RGB color space cform2srgb = makecform('lab2srgb'); J = applycform(LAB, cform2srgb); % Display the results figure, imshow(RGB); figure, imshow(J);
- 8. histeq • Enhance contrast using histogram equalization Syntax J = histeq(I, hgram) J = histeq(I, n) [J, T] = histeq(I,...) newmap = histeq(X, map, hgram) newmap = histeq(X, map) [newmap, T] = histeq(X,...) Description • histeq enhances the contrast of images by transforming the values in an intensity image, or the values in the colormap of an indexed image, so that the histogram of the output image approximately matches a specified histogram.
- 9. Examples • Enhance the contrast of an intensity image using histogram equalization. I = imread('tire.tif'); J = histeq(I); imshow(I) figure, imshow(J) • Display a histogram of the original image. figure; imhist(I,64) • Compare it to a histogram of the processed image. figure; imhist(J,64)
- 10. imadjust • Adjust image intensity values or colormap Syntax J = imadjust(I) J = imadjust(I,[low_in; high_in],[low_out; high_out]) J = imadjust(I,[low_in; high_in],[low_out; high_out],gamma) newmap = imadjust(map,[low_in; high_in],[low_out; high_out],gamma) RGB2 = imadjust(RGB1,...)
- 11. Examples • Adjust a low-contrast grayscale image. I = imread('pout.tif'); J = imadjust(I); imshow(I), figure, imshow(J) • Adjust the grayscale image, specifying the contrast limits. K = imadjust(I,[0.3 0.7],[]); figure, imshow(K) • Adjust an RGB image. RGB1 = imread('football.jpg'); RGB2 = imadjust(RGB1,[.2 .3 0; .6 .7 1],[]); imshow(RGB1), figure, imshow(RGB2)
- 12. imnoise • Add noise to image Syntax J = imnoise(I,type) J = imnoise(I,type,parameters) J = imnoise(I,'gaussian',m,v) J = imnoise(I,'localvar',V) J = imnoise(I,'localvar',image_intensity,var) J = imnoise(I,'poisson') J = imnoise(I,'salt & pepper',d) J = imnoise(I,'speckle',v)
- 13. Examples I = imread('eight.tif'); J = imnoise(I,'salt & pepper',0.02); figure, imshow(I) figure, imshow(J)
- 14. medfilt2 • 2-D median filtering Syntax B = medfilt2(A, [m n]) B = medfilt2(A) B = medfilt2(A, 'indexed', ...) B = medfilt2(..., padopt) Description Median filtering is a nonlinear operation often used in image processing to reduce "salt and pepper" noise. A median filter is more effective than convolution when the goal is to simultaneously reduce noise and preserve edges.
- 15. Examples • Add salt and pepper noise to an image and then restore the image using medfilt2. I = imread('eight.tif'); J = imnoise(I,'salt & pepper',0.02); K = medfilt2(J); imshow(J), figure, imshow(K)
- 16. Questions?
Editor's Notes
- #4: Image arithmetic is the implementation of standard arithmetic operations, such as addition, subtraction, multiplication, and division, on images. Image arithmetic has many uses in image processing both as a preliminary step in more complex operations and by itself. For example, image subtraction can be used to detect differences between two or more images of the same scene or object.