Fuzzy dissimilarity color histogram equalization for contrast enhancement and color correction

Many statistical histogram-based methods perform intensity transformation on gray levels in the statistical histogram. This may cause over-enhancement due to dominating portions of the histogram. Various methods tackle this problem by overwhelming the dominating portions and improving the inferior components. Though, this may change the natural appearances of the image and results in degraded visual quality. In order to attenuate such limitations, an efficient method called Fuzzy Dissimilarity Adaptive Histogram Equalization with Gamma Correction (FDAHE-GC) algorithm is proposed. In this work, a Fuzzy Dissimilarity Histogram (FDH) is obtained from the neighborhood characteristics of an intensity. An intensity mapping function, constructed from FDH is applied to enhance the contrast and natural characteristics of an image. Finally, the gamma correction is employed to enhance the dark regions. In order to tune the fine details and to improve visual appearance of an image, the proposed FDAHE-GC algorithm is applied to the intensity value of HSI space. The performance of the presented method is evaluated with different existing methods using image quality assessment tools such as entropy, Colorfulness (C), Hue Deviation Index (HDI), Saturation, Contrast Enhancement Factor (CEF) and Gradient (G). The investigational results tested on standard benchmark test images with visual inspection shows the superiority of the proposed FDAHE-GC algorithm. (C) 2020 Elsevier B.V. All rights reserved.