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Abstract
In statistics, a histogram is a graphical representation of the distribution of data. It is an estimate of the probability distribution of a continuous variable. The histogram of an image represents the relative frequency of occurrence of the various gray levels in the image. Image enhancement algorithms based on Histogram equalization (HE) often fall short to maintain the image quality after enhancement due to quantum jump in the cumulative distribution function (CDF) in the histogram. Moreover, some detail parts appear to be washed out after enhancement. To solve this problem, various histogram equalization methods were introduced which enhanced the image details parts separately and combine it with the enhanced image using a weighted function. This gives a way to control the enhancement of the details improving the quality of the image. Experiments show that the proposed method performs well as compared to the existing enhancement algorithms. Histogram equalization methods preserve the image brightness, local contrast of the image and reduce the noise in speech.