A Novel Fuzzy Logic Approach to Image Contrast Enhancement and Brightness Preserving
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Histogram Equalization (HE) is one of the most commonly used methods for image contrast enhancement. However, HE and most other contrast enhancement methods may produce un-natural looking images and the images obtained by these methods are not desirable in applications such as consumer electronic products where brightness preservation is necessary to avoid annoying artifacts. To solve such problems, Brightness preserving Fuzzy Histogram Equalization (BFHE) is proposed for image contrast enhancement. The BFHE 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 sub-histograms based on the value of Absolute mean brightness error (AMBE) and then equalizes them independently to preserve image brightness. The experimental results show that the BFHE method preserves more brightness and gives natural looking images than the conventional methods. The proposed method has been tested using several images and gives better visual quality as compared to the conventional methods. Moreover, Average Information Contents (AIC) and Contrast Improvement Index (CII) are used to evaluate image quality
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