Contrast-limited adaptive histogram equalization (CLAHE)
J = adapthisteq(I)
J = adapthisteq(I,Name,Value)
Apply CLAHE to an image and display the results.
I = imread('tire.tif'); J = adapthisteq(I,'clipLimit',0.02,'Distribution','rayleigh'); imshowpair(I,J,'montage'); title('Original Image (left) and Contrast Enhanced Image (right)')
Read the indexed color image into the workspace.
[X, MAP] = imread('shadow.tif');
Convert the indexed image into a truecolor (RGB) image, then convert the RGB image into the L*a*b* color space.
RGB = ind2rgb(X,MAP); LAB = rgb2lab(RGB);
Scale values to the range expected by the
adapthisteq function, [0 1].
L = LAB(:,:,1)/100;
Perform CLAHE on the L channel. Scale the result to get back to the range used by the L*a*b* color space.
L = adapthisteq(L,'NumTiles',[8 8],'ClipLimit',0.005); LAB(:,:,1) = L*100;
Convert the resulting image back into the RGB color space.
J = lab2rgb(LAB);
Display the original image and the processed image.
figure imshowpair(RGB,J,'montage') title('Original (left) and Contrast Enhanced (right) Image')
Shadows in the enhanced image look darker and highlights look brighter. The overall contrast is improved.
I— Input Image
Input intensity image, specified as a numeric 2-D array.
comma-separated pairs of
the argument name and
Value is the corresponding value.
Name must appear inside single quotes (
' '). You can
specify several name and value pair arguments in any order as
'NumTiles',[8 16]divides the image into 8 rows and 16 columns of tiles.
'NumTiles'— Number of tiles
[8,8](default) | 2-element vector of positive integers
Number of rectangular contextual regions (tiles) into which
divides the image, specified as a 2-element vector of positive integers.
With the original image divided into
M rows and
N columns of tiles, the value of
[M N]. Both
N must be at least
2. The total number of tiles is equal to
M*N. The optimal number of tiles depends on the
type of the input image, and it is best determined through
'ClipLimit'— Contrast enhancement limit
0.01(default) | real scalar
Contrast enhancement limit, specified as a real scalar in the range [0, 1]. Higher limits result in more contrast.
'ClipLimit' is a contrast factor that prevents
oversaturation 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
'NBins'— Number of histogram bins used to build a contrast enhancing transformation
256(default) | positive integer scalar
Number of histogram bins used to build a contrast enhancing transformation, specified as a positive integer scalar. Higher values result in greater dynamic range at the cost of slower processing speed.
'Range'— Range of output data
Range of the output image data, specified as one of the following values:
|Use the full range of the output class (e.g. [0 255]
|Limit the range to |
'Distribution'— Desired histogram shape
Desired histogram shape, specified as one of the following values:
|Create a flat histogram.|
|Create a bell-shaped histogram.|
|Create a curved histogram.|
'Distribution' specifies the distribution that
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.
'Alpha'— Distribution parameter
0.4(default) | nonnegative real scalar
Distribution parameter, specified as a nonnegative real scalar.
only used when
'Distribution' is set to
CLAHE operates on small regions in the image, called tiles,
rather than the entire image.
adapthisteq calculates the contrast
transform function for each tile individually. Each tile's contrast is enhanced, so
that the histogram of the output region approximately matches the histogram specified by
' value. 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.
 Zuiderveld, Karel. “Contrast Limited Adaptive Histograph Equalization.” Graphic Gems IV. San Diego: Academic Press Professional, 1994. 474–485.