A Novel Contrast Enhancement Technique using Gradient-Based Joint Histogram Equalization

Abstract

Image enhancement by histogram equalization reduces the number of gray levels that lead to information loss and unnatural appearance. This paper aims to improve the contrast and preserve information and edge details by employing gradient-based joint histogram equalization. It is achieved by a multiscale-based dark pass filter, which gives the pixel’s edge information. A joint histogram is computed from the edge information and the gray-level distribution of the low contrast image to develop a discrete function. This discrete function is mapped to uniform distribution to get the final enhanced image. The proposed method is experimented on Kodak, USC-SIPI, and CSIQ databases and analyzed using various performance measures such as Contrast, standard deviation, contrast improvement index, structural similarity index, normalized entropy, and normalized mean brightness error. It is observed that the proposed method provides the highest Contrast values of 86.2, 85.79, and 86.02 in Kodak, USC-SIPI, and CSIQ databases, respectively. Normalized entropy value is found to be highest for the proposed method for all the databases. This is noticed to be 0.89, 0.84, and 0.85 for the databases Kodak, USC-SIPI, and CSIQ, respectively. The degree of the uniform distribution is measured by Kullback–Leibler distance. The proposed method produces more uniformity than other techniques available in the literature for all the three databases.