Advantages And Disadvantages Of Histogram Equalization
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A. Histogram Equalization Histogram equalization is a method in image processing which is used for adjusting the contrast of an image using image’s histogram. This method usually increases the overall contrast of many images, especially when the usable data of the image is represented by close contrast values. This adjustment allows the intensities to be distributed better on the histogram. This allows areas of lower contrast to gain a higher contrast. Histogram equalization accomplishes this by spreading out the most frequent intensity values effectively.
Modifications of this method use multiple histograms, called sub histograms, to emphasize local contrast, rather than the overall contrast.
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AHE is a computer based image processing technique. It varies from ordinary histogram equalization in the respect that the adaptive method computes several histograms, each corresponding to a distinct section of the image which is used to redistribute the lightness value of the image. Therefore, it is suitable for improving the local contrast and enhancing the definitions of edges in each region of an image. However, AHE has a tendency to over amplify the noise in relatively homogeneous regions of an …show more content…
This feature can also be applied to global histogram equalization which gives rise to contrast limited histogram equalization (CLAHE), which is rarely used in practice. In CLAHE, the contrast limiting procedure has to be applied for each neighbourhood pixel from which a transformation function is derived. CLAHE was developed to prevent the over amplification of noise that adaptive histogram equalization produced. This can be obtained by limiting the contrast enhancement of AHE. The contrast amplification in the vicinity of a given pixel value is given by the slope of the transformation function. It is directly proportional to the slope of the neighbourhood cumulative distribution function also known as CDF and therefore to the value of the histogram at that particular pixel value. CLAHE limits the amplification by clipping the histogram at a predefined threshold before computing the CDF. This limits the slope of the CDF and therefore the transformation function is limited. The value at which the histogram is clipped, called the clip limit, depends on the normalization of the histogram and thereby on the size of the neighbourhood
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