Recursively Partitioned Clipped Histogram Equalization Techniques for Preserving Image Features

In this work, we present recursively partitioned clipped histogram equalization (RPCHE) techniques, viz., recursive median partitioned clipped histogram equalization (RMDPCHE) and recursive mean partitioned clipped histogram equalization techniques for better quality images. They adopt the feature of histogram equalization (HE) in terms of simplicity, the feature of recursive histogram partition and histogram equalization to maintain low absolute mean brightness error and the feature of clipped histogram equalization in terms of control on over enhancement. In addition to these, the RPCHE methods are devoid of intensity compression, resulting in no gray level loss, and retain the total number of gray, assure uniform degree enhancement of gray to get over all image enhancement and assure no false contouring of objects. In RMDPCHE, the histogram is divided recursively with median gray level, and later these sub histograms are restricted to individual clipped threshold and finally conventional HE is applied on these clipped histograms to get over all equalized image. Experimental results show the superiority of the proposed methods over the state-of-art HE methods in terms of preserving image features with uniform degree of enhancement. They are able to achieve maximum entropy with minimum gradient magnitude similarity deviation. These ensure the objects in the processed image to have fine contours with natural enhancement.