Fuzzy-Based Histogram Partitioning for Bi-Histogram Equalisation of Low Contrast Images

The conventional histogram equalisation (CHE), though being simple and widely used technique for contrast enhancement, but fails to preserve the mean brightness and natural appearance of images. Most of the improved histogram equalisation (HE) methods give better performance in terms of one or two metrics and sacri ce their performance in terms of other metrics. In this paper, a novel fuzzy based bi-HE method is proposed which equalises low contrast images optimally in terms of all considered metrics. The novelty of the proposed method lies in selection of fuzzy threshold value using level-snip technique which is then used to partition the histogram into segments. The segmented sub-histograms, like other bi-HE methods, are equalised independently and are combined together. Simulation results show that for widerange of test images, the proposed method improves the contrast while preserving other characteristics and provides good trade-off among all the considered performance metrics.This work was supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under Grant DF-374-135-1441