Fuzzy Logic-Based Histogram Equalization for Image Contrast Enhancement

Abstract

Fuzzy logic-based histogram equalization (FHE) is proposed for image contrast enhancement. The FHE consists of two stages. First, fuzzy histogram is computed based on fuzzy set theory to handle the inexactness of gray level values in a better way compared to classical crisp histograms. In the second stage, the fuzzy histogram is divided into two subhistograms based on the median value of the original image and then equalizes them independently to preserve image brightness. The qualitative and quantitative analyses of proposed FHE algorithm are evaluated using two well-known parameters like average information contents (AIC) and natural image quality evaluator (NIQE) index for various images. From the qualitative and quantitative measures, it is interesting to see that this proposed method provides optimum results by giving better contrast enhancement and preserving the local information of the original image. Experimental result shows that the proposed method can effectively and significantly eliminate washed-out appearance and adverse artifacts induced by several existing methods. The proposed method has been tested using several images and gives better visual quality as compared to the conventional methods.

MRI brain image enhancement using brightness preserving adaptive fuzzy histogram equalization

“…Fuzzy image enhancement is based on gray level mapping into a fuzzy plane, using a membership transformation function . By using fuzzy technique, contrast improved image is generated by providing larger weight to the gray levels that are closer to the mean gray level of the image.…”

Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm

“…Recent literature illustrates that the heuristic and metaheuristic algorithms such as particle swarm optimization (PSO) [20][21][22][23][24][25], bacterial foraging algorithm (BFO) [1,13,17,18], differential evaluation (DE) [19,[26][27][28], artificial bee colony (ABC) [11,29], cuckoo search (CS) [12,30], watershed algorithm [31], fuzzy logic [32], hybrid method [33], and self-adaptive parameter optimization algorithm [34] are widely considered for optimal multilevel image segmentation problem to enhance the outcome.…”

A Novel Enhancement Algorithm Combined with Improved Fuzzy Set Theory for Low Illumination Images

“…Traditional image enhancement methods can be mainly divided into two categories: (1) the global methods: including logarithmic transformation, Gamma correction, piecewise linear transformation, and histogram equalization [1][2][3][4]; without the brightness difference among neighborhood, these methods changed the gray levels of pixels by one-toone mapping; when low illumination images were enhanced only by global methods, the contrast was effectively improved but the local details features were ignored easily; finally, enhanced images showed fuzzy phenomenon; (2) the local methods: enhancing local contrast based on the local features and mainly including multiscale Retinex with color restoration and gradient domain methods [5][6][7]. The Retinex theory has been widely paid attention to.…”