adapthisteq() - Image Processing

J = adapthisteq(I) enhances the contrast of the grayscale image I by transforming the values using contrast-limited adaptive histogram equalization (CLAHE).
CLAHE operates on small regions in the image, called tiles, rather than the entire image.
Each tile's contrast is enhanced, so that the histogram of the output region approximately matches the histogram specified by the 'Distribution' parameter.
The neighboring tiles are then combined using bilinear interpolation to eliminate artificially induced boundaries.
The contrast, especially in homogeneous areas, can be limited to avoid amplifying any noise that might be present in the image.
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.
Parameter - Value
'NumTiles' - Two-element vector of positive integers specifying the number of tiles by row and column, [M N]. Both M and N must be at least 2. The total number of tiles is equal to M*N.    Default: [8 8]
'ClipLimit' -  Real scalar in the range [0 1] that specifies a contrast enhancement limit. Higher numbers result in more contrast.     Default: 0.01 
'NBins' - Positive integer scalar specifying the number of bins for the histogram used in building a contrast enhancing transformation. Higher values result in greater dynamic range at the cost of slower processing speed.    Default: 256
'Range' -  String specifying the range of the output image data.     Default: 'full'
'original' - Range is limited to the range of the original image, [min(I(:)) max(I(:))].
'full' - Full range of the output image class is used. For example, for uint8 data, range is [0 255].
'Distribution' - String specifying the desired histogram shape for the image tiles.     Default: 'uniform'
'uniform' - Flat histogram
'rayleigh' - Bell-shaped histogram
'exponential' - Curved histogram
'Alpha' - Nonnegative real scalar specifying a distribution parameter.    Default: 0.4 
Note : Only used when 'Distribution' is set to either 'rayleigh' or 'exponential'.

Syntax

J = adapthisteq(I)
J = adapthisteq(I,param1,val1,param2,val2...)

Example

%Apply Contrast-limited Adaptive Histogram Equalization (CLAHE)Open This Example
%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
%Read the color image into the workspace.
[X MAP] = imread('shadow.tif');
%Convert the indexed image into a truecolor (RGB) image.
RGB = ind2rgb(X,MAP);
%Convert the RGB image into the L*a*b* color space.
cform2lab = makecform('srgb2lab');
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 the resultant image back into the RGB color space.
cform2srgb = makecform('lab2srgb');
J = applycform(LAB, cform2srgb);
%Display the original image and result.
figure, imshow(RGB);
figure, imshow(J);

Output / Return Value

Limitations

Alternatives / See Also

Reference