BFAGC: A Bias-Free Adaptive Gamma Correction Method for Image Enhancement

Abstract

One of the most interesting and visually appealing areas of image processing is image enhancement. In order to enhance the images, various methods have already been introduced. But, these existing enhancement methods are hardly able to enhance all types of images. To address this issue, Adaptive Gamma Correction (AGC) method has been introduced which classifies images into several classes based on the statistical information of the image and then applies an adaptive gamma correction method. However, AGC method can be affected by outlier pixels and fails to appropriately enhance the images. To solve this problem, we propose an improved version of Adaptive Gamma Correction (AGC) namely Bias-Free Adaptive Gamma Correction (BFAGC). The proposed BFAGC is rarely affected by the outlier pixels. The extensive experimental analysis has been performed to evaluate the efficacy of the proposed BFAGC method. Both the qualitative and quantitative evaluation metrics have shown that BFAGC produces comparatively better enhanced images than AGC and other existing state-of-the-art methods.