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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 891864, 10 pages
http://dx.doi.org/10.1155/2013/891864
Research Article

Fuzzy Logic-Based Histogram Equalization for Image Contrast Enhancement

1Department of Electronics and Communication Engineering, P.S.N.A College of Engineering and Technology, Dindigul, Tamil Nadu 624622, India
2Ratnavel Subramaniam (RVS) College of Engineering and Technology, Dindigul, Tamil Nadu 624005, India

Received 23 May 2013; Revised 27 June 2013; Accepted 27 June 2013

Academic Editor: Erik Cuevas

Copyright © 2013 V. Magudeeswaran and C. G. Ravichandran. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.