An Efficient Image Contrast Enhancement using Segmentation in Genetic Algorithm for Remote Sensing Applications

This paper proposes a novel optimized histogram equalization technique using a genetic algorithm (GA) for brightness-preserved contrast enhancement. This method optimizes the histogram equalization technique to extract detailed data from distorted images. The principal objective is to enhance histogram equalization by fixing a trade-off between contrast improvement and mean-brightness preservation. A genetic algorithm is used to generate the new target histogram. The optimized histogram is used in the histogram equalization (HE) process to generate the final enhanced image. The performance of the proposed method is checked and validated with qualitative and quantitative assessments against well-known state-of-the-art techniques. The result section depicts that the proposed method significantly enhances the visual effect of distorted images based on human subjective and objective evaluation indices. 3-5 percent enhancement can be observed if we compare the standard Histogram Equalization method to the proposed method.