Introduction Contrast Limited AHE (CLAHE) differs from ordinary adaptive histogram equalization in its contrast limiting. This feature can also be applied to global histogram equalization, giving rise to contrast limited histogram equalization (CLHE), which is rarely used in practice. In the case of CLAHE, the contrast limiting procedure has to be applied for each neighborhood from which a transformation function is derived. CLAHE was developed to prevent the over amplification of noise that adaptive histogram equalization can give rise to.
Description 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 tiles 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.
Cont… The contrast, especially in homogeneous areas, can be limited to avoid amplifying any noise that might be present in the image.
Remarks Real scalar in the range [0 1], thatClip limit specifies the contrast enhancement limit. Higher number result in more contrast. Default 0.01. String specify the desires histogramDistribution shape for the image tiles. • uniform-flat histogram •Rayleigh-bell shaped histogram •Exponential-curved histogram Default „uniform‟.
Algorithm Obtain all the inputs: Image, Number of regions in row and column directions, Number of bins for the histograms used in building image transform function (dynamic range), Clip limit for contrast limiting (normalized from 0 to 1). Pre-process the inputs: Determine real clip limit from the normalized value if necessary, pad the image before splitting it into regions. Process each contextual region (tile) thus producing gray level mappings: Extract a single image region, make a histogram for this region using the specified number of bins, clip the histogram using clip limit, create a mapping (transformation function) for this region. Interpolate gray level mappings in order to assemble final CLAHE image: Extract cluster of four neighboring mapping functions, process image region partly overlapping each of the mapping tiles, extract a single pixel, apply four mappings to that pixel, and interpolate between the results to obtain the output pixel; repeat over the entire image.
Classes supported by CLAHE Class Support  :- Grayscale image I can be of class uint8, uint16, int16, single, or double. The output image J has the same class as I.
Example of CLAHE Coding :- Apply Contrast-limited Adaptive Histogram Equalization (CLAHE) to an image and display the results. I = imread(„a.jpg); A = adapthisteq(I,clipLimit,0.02,Distribution,rayleigh); figure, imshow(I); figure, imshow(A);
Application areas of CLAHE Contrast Enhancement for Mammogram Images: to highlight the finer hidden details in mammogram images and to adjust the level of contrast enhancement. Brightness preserving contrast enhancement of medical images. Face identification using CLAHE. Contrast limited adaptive histogram specification (CLAHS) to deal with the inherent non uniform lighting in underwater imagery. Chest computer tomography (CT) images
Advantages CLAHE was developed to prevent the over amplification of noise that adaptive histogram equalization can give rise to. CLAHE, though able to increase contrast more than other techniques. It introduces large changes in the pixel gray levels. CLAHE may lead to introduction of the processing artifacts and affect of decision making process.
Disadvantages It operates on small data regions (tiles), rather than the entire image. It is computationally expensive (in software). It is quite complex (in hardware). Implementing recursion in hardware can be complex, necessitating the implementation of control flow and of storage for intermediate results. Time-consuming, as recursions are performed sequentially.
References Introduction, “Wikipedia.com”. Specification, “ MATLAB”. Description, “MATLAB”. Remarks, “MATLAB”. Algorithm, “Rajesh Garg, Bhawna Mittal, Sheetal Garg, “Histogram Equalization Techniques For Image Enhancement”, IJECT Vol. 2, Issue 1, March 2011. Class Support, “MATLAB”. Advantage, CLAHE was developed to, “Wikipedia.com”. Advantage, CLAHE ,though able to, “slide share.com”.