Contrast Enhancement Techniques

The Image Processing Toolbox contains several image enhancement routines. Three functions are particularly suitable for contrast enhancement: imadjust, histeq, and adapthisteq. This demo compares their use for enhancing grayscale and truecolor images.

Contents

• Step 1: Load images
• Step 2: Resize images
• Step 3: Enhance grayscale images
• Step 4: Enhance color images

Step 1: Load images

Read in two grayscale images: pout.tif and tire.tif. Also read in an indexed RGB image: shadow.tif.

pout = imread('pout.tif');
tire = imread('tire.tif');
[X map] = imread('shadow.tif');
shadow = ind2rgb(X,map); % convert to truecolor

Step 2: Resize images

To make the image comparison easier, resize the images to have the same width. Preserve their aspect ratios by scaling their heights.

width = 210;
images = {pout, tire, shadow};

for k = 1:3
  dim = size(images{k});
  images{k} = imresize(images{k},[width*dim(1)/dim(2) width],'bicubic');
end

pout = images{1};
tire = images{2};
shadow = images{3};

Step 3: Enhance grayscale images

Using the default settings, compare the effectiveness of the following three techniques:

• imadjust increases the contrast of the image by mapping the values of the input intensity image to new values such that, by default, 1% of the data is saturated at low and high intensities of the input data.
• histeq performs histogram equalization. It enhances the contrast of images by transforming the values in an intensity image so that the histogram of the output image approximately matches a specified histogram (uniform distribution by default).
• adapthisteq performs contrast-limited adaptive histogram equalization. Unlike histeq, it operates on small data regions (tiles) rather than the entire image. Each tile's contrast is enhanced so that the histogram of each output region approximately matches the specified histogram (uniform distribution by default). The contrast enhancement can be limited in order to avoid amplifying the noise which might be present in the image.

pout_imadjust = imadjust(pout);
pout_histeq = histeq(pout);
pout_adapthisteq = adapthisteq(pout);

imshow(pout);
title('Original');

figure, imshow(pout_imadjust);
title('Imadjust');

figure, imshow(pout_histeq);
title('Histeq');

figure, imshow(pout_adapthisteq);
title('Adapthisteq');

tire_imadjust = imadjust(tire);
tire_histeq = histeq(tire);
tire_adapthisteq = adapthisteq(tire);

figure, imshow(tire);
title('Original');

figure, imshow(tire_imadjust);
title('Imadjust');

figure, imshow(tire_histeq);
title('Histeq');

figure, imshow(tire_adapthisteq);
title('Adapthisteq');

Notice that imadjust had little effect on the image of the tire, but it caused a drastic change in the case of pout. Plotting the histograms of pout.tif and tire.tif reveals that most of the pixels in the first image are concentrated in the center of the histogram, while in the case of tire.tif, the values are already spread out between the minimum of 0 and maximum of 255 thus preventing imadjust from being effective in adjusting the contrast of the image.

figure, imhist(pout), title('pout.tif');
figure, imhist(tire), title('tire.tif');

Histogram equalization, on the other hand, substantially changes both images. Many of the previously hidden features are exposed, especially the debris particles on the tire. Unfortunately, at the same time, the enhancement over-saturates several areas of both images. Notice how the center of the tire, part of the child's face, and the jacket became washed out.

Concentrating on the image of the tire, it would be preferable for the center of the wheel to stay at about the same brightness while enhancing the contrast in other areas of the image. In order for that to happen, a different transformation would have to be applied to different portions of the image. The Contrast-Limited Adaptive Histogram Equalization technique, implemented in adapthisteq, can accomplish this. The algorithm analyzes portions of the image and computes the appropriate transformations. A limit on the level of contrast enhancement can also be set, thus preventing the over-saturation caused by the basic histogram equalization method of histeq. This is the most sophisticated technique in this demonstration.

Step 4: Enhance color images

Contrast enhancement of color images is typically done by transforming an image to a color space that has image intensity as one of its components. One such color space is L*a*b*. Use color transform functions to convert the image from RGB to L*a*b* color space, and then work on the luminosity layer 'L*' of the image. Manipulating luminosity affects the intensity of the pixels, while preserving the original colors.

srgb2lab = makecform('srgb2lab');
lab2srgb = makecform('lab2srgb');

shadow_lab = applycform(shadow, srgb2lab); % convert to L*a*b*

% the values of luminosity can span a range from 0 to 100; scale them
% to [0 1] range (appropriate for MATLAB intensity images of class double) 
% before applying the three contrast enhancement techniques
max_luminosity = 100;
L = shadow_lab(:,:,1)/max_luminosity;

% replace the luminosity layer with the processed data and then convert
% the image back to the RGB colorspace
shadow_imadjust = shadow_lab;
shadow_imadjust(:,:,1) = imadjust(L)*max_luminosity;
shadow_imadjust = applycform(shadow_imadjust, lab2srgb);

shadow_histeq = shadow_lab;
shadow_histeq(:,:,1) = histeq(L)*max_luminosity;
shadow_histeq = applycform(shadow_histeq, lab2srgb);

shadow_adapthisteq = shadow_lab;
shadow_adapthisteq(:,:,1) = adapthisteq(L)*max_luminosity;
shadow_adapthisteq = applycform(shadow_adapthisteq, lab2srgb);

figure, imshow(shadow);
title('Original');

figure, imshow(shadow_imadjust);
title('Imadjust');

figure, imshow(shadow_histeq);
title('Histeq');

figure, imshow(shadow_adapthisteq);
title('Adapthisteq');

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