Brightness preserving fuzzy dynamic histogram equalization

Abstract—Image enhancement is a fundamental step of image processing and machine vision to improve the quality of an image for a specific application. Histogram equalization is an attractive and commonly-employed image enhancement algorithm which is used in certain circumstances because of its global nature. Brightness Preserving Dynamic Histogram Equalization (BPDHE) overcomes this problem by considering the local image histogram. However, this algorithm can result in false countering and ignoring of details. False countering is the result of dedicating wide intervals to intensities with high probability; ignoring details results from the wide distribution of regions with detailed information in small regions. This paper introduces a fuzzy version of BPDHE (i.e., BPFDHE) to overcome the aforementioned problems. The fuzzification is employed to provide a crisper version of an interval and of the number of pixels in that interval. This algorithm has been tested on 30 images under several different conditions. The results with BPFDHE, in terms of subjective quality, outperform histogram equalization and BPDHE