Salp Swarm Algorithm-Based Optimally Weighted Histogram Framework for Image Enhancement
Ashish Kumar Bhandari, Pankaj Kandhway, Shubham Maurya
Abstract
This article introduces a novel optimally selected plateau limit (PL)-based histogram modification framework. This approach preserves the brightness and improves the contrast of an image effectively without introducing absurd visual deterioration, unnatural contrast effects, and structural artifacts. It also enhances the weak illumination situations, such as backlighting effect and the nonuniform illumination of images without introducing any undesirable artifacts. The proposed method based on the subhistogram and clipping operations utilizes the PLs to modify the histogram of the image before applying the histogram equalization approach. The salp swarm algorithm (SSA)-based optimization technique is incorporated to compute the optimal PLs or adaptive weighted limits. To prove the efficiency of the proposed algorithm, a comparative study is done with the well-known histogram-based processing techniques and state-of-art methods in the literature. Furthermore, well-recognized different evaluation parameters are considered to compare the proposed framework with other existing methods.