Image contrast improvement through a metaheuristic scheme

作     者：Mukhopadhyay, Souradeep Hossain, Sabbir Malakar, Samir Cuevas, Erik Sarkar, Ram 

作者机构：Jadavpur Univ Dept Comp Sci & Engn Kolkata India Asutosh Coll Dept Comp Sci Kolkata India Univ Guadalajara Dept Elect Guadalajara Mexico 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2023年第27卷第18期

页      面：13657-13676页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Center for Microprocessor Applications for Training Education and Research 

主　　题：Contrast enhancement Incomplete beta function Meta-heuristic Artificial electric field algorithm Kodak dataset MIT-Adobe FiveK dataset USC-SIPI dataset 

摘      要：Contrast enhancement is an important pre-processing task for several image and video processing applications. The objective of a contrast enhancement method is to improve the quality of the visual information contained in the images for further processing. Due to the enormous challenges, it is still considered as an open research problem. Several approaches have been proposed in the literature based on spatial and fr uency domain techniques. Among them, the Incomplete Beta Function (IBF) is a popular scheme used for image contrast enhancement. In the IBF based image contrast enhancement technique, quality of an image is improved by two controlling parameters. Under such conditions, these parameters need to be tuned for obtaining better outcomes. In this paper, a new gray-scale contrast enhancement algorithm is introduced where, instead of tuning the controlling parameters of IBF experimentally, their near-optimal values are calculated with the help of a recently published meta-heuristic algorithm called Artificial Electric Field Algorithm (AEFA). The proposed method has been compared with many state-of-the-art techniques in terms of some standard metrics considering three different datasets, namely Kodak, MIT-Adobe FiveK and USC-SIPI. Simulation results demonstrate that the proposed AEFA based image enhancement technique increases the overall image contrast and enriches the information present in the image.