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1. 1. Contrast Limited Adaptive Histogram Equalization. BY. NANCY(CS-1212) NISHU(CS-1219)
2. 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]
4. 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 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.
5. 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. 6. 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‟.[4]
7. 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. 8. Flow chart of an Algorithm
9. 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. 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. 11. Input image (I)
12. 12. Output image (A)
13. 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. 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. 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. 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. 17. THANX
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