A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model

Abstract

Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over-enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over-enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high-frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel-level image fusion method to further reduce the phenomenon of over-enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state-of-the-art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over-enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image.