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

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