1. Contrast Limited Adaptive
Histogram Equalization.
BY.
NANCY(CS-1212)
NISHU(CS-1219)

2. 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.[1]

3. Specification
 Syntax of CLAHE [2]
 J = adapthisteq(I)
 J = adapthisteq(I,param1,val1,param2,val2...)
J = adapthisteq(I,'clipLimit',0.02,'Distribution','rayleigh')

4. 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 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.

5. Cont…
 The contrast, especially in homogeneous areas, can be
limited to avoid amplifying any noise that might be present
in the image.[3]

6. Remarks
Real scalar in the range [0 1], that
Clip limit specifies the contrast enhancement
limit. Higher number result in more
contrast.
Default 0.01.
String specify the desires histogram
Distribution shape for the image tiles.
• uniform-flat histogram
•Rayleigh-bell shaped histogram
•Exponential-curved histogram
Default „uniform‟.[4]

7. 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.[5]

8. Flow chart of an Algorithm

9. Classes supported by CLAHE
 Class Support [6] :-
 Grayscale image I can be of class uint8, uint16, int16,
single, or double.
 The output image J has the same class as I.

10. 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);

11. Input image (I)

12. Output image (A)

13. 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

14. Advantages
 CLAHE was developed to prevent the over amplification of
noise that adaptive histogram equalization can give rise
to.[7]
 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.[8]

15. 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.

16. References
[1] Introduction, “Wikipedia.com”.
[2] Specification, “ MATLAB”.
[3] Description, “MATLAB”.
[4] Remarks, “MATLAB”.
[5] Algorithm, “Rajesh Garg, Bhawna Mittal, Sheetal Garg,
“Histogram Equalization Techniques For Image
Enhancement”, IJECT Vol. 2, Issue 1, March 2011.
[6] Class Support, “MATLAB”.
[7] Advantage, CLAHE was developed to, “Wikipedia.com”.
[8] Advantage, CLAHE ,though able to, “slide share.com”.

17. THANX