Contrast-limited adaptive histogram equalization (CLAHE)
J = adapthisteq(I)
J = adapthisteq(I,param1,val1,param2,val2...)
J = adapthisteq(I) enhances
the contrast of the grayscale image
I by transforming
the values using contrast-limited adaptive histogram equalization
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
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.
Two-element vector of positive integers specifying the
number of tiles by row and column,
Real scalar in the range [0 1] that specifies a contrast enhancement limit. Higher numbers result in more contrast.
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.
Range of the output image data, specified as one of the following:
Desired histogram shape for the image tiles, specified as one of the following:
Nonnegative real scalar specifying a distribution parameter.
I can be of class
double. The output image
the same class as
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);
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.
LAB = rgb2lab(RGB);
Scale values to the range expected by the
L = LAB(:,:,1)/100;
Perform CLAHE. Multiply the result to get back to the range used by the L*a*b* color space.
LAB(:,:,1) = adapthisteq(L,'NumTiles',... [8 8],'ClipLimit',0.005)*100;
Convert the resultant image back into the RGB color space.
J = lab2rgb(LAB);
Display the original image and result.
figure, imshow(RGB); figure, imshow(J);
'NumTiles' specifies the number
of rectangular contextual regions (tiles) into which
adapthisteq calculates the contrast
transform function for each of these regions individually. The optimal
number of tiles depends on the type of the input image, and it is
best determined through experimentation.
'ClipLimit' is a contrast factor
that prevents over-saturation of the image specifically in homogeneous
areas. These areas are characterized by a high peak in the histogram
of the particular image tile due to many pixels falling inside the
same gray level range. Without the clip limit, the adaptive histogram
equalization technique could produce results that, in some cases,
are worse than the original image.
'Distribution' specifies the distribution
adapthisteq uses as the basis for creating
the contrast transform function. The distribution you select should
depend on the type of the input image. For example, underwater imagery
appears to look more natural when the Rayleigh distribution is used.
 Zuiderveld, Karel. "Contrast Limited Adaptive Histograph Equalization." Graphic Gems IV. San Diego: Academic Press Professional, 1994. 474–485.