Exposure and Median Based One-to-One Gray Level Mapping Transformation for Entropy Preservation and Contrast Enhancement

Abstract

Many histogram equalization (HE) techniques have been proposed for the contrast enhancement in the past. In recent years clipped histogram equalization techniques are developed to control the degree of over enhancement and the noise. Yet these methods are not guaranteed to preserve the gray levels and thus the information in output image is less than that in the input image, even though it has been enhanced. We propose two new one-to-one gray level mapping (OGM) transformation methods, namely exposure based one-to-one gray level mapping (EOGM) transformation and median based one-to-one gray level mapping (MOGM) transformation. In EOGM and MOGM methods histogram is divided into two sub histograms based on exposure and median of the images respectively. Weights for these sub histograms are calculated and then OGM transformation function is applied to these sub histograms by using the derived weights. This transformation addresses both over enhancement and gray level loss effectively and also ensure uniform degree of enhancement. This preserves all the information content even after enhancement with all structural details, ensures no false contouring. Thus they are suitable for medical image applications, where information loss leads to wrong diagnosis. The experimental results show the supremacy of our methods over existing HE methods.