Structure-priority image restoration through genetic algorithm optimization

• Main Authors: WANG, Zhaoxia, PEN, Haibo, YANG, Ting, WANG, Quan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Genetic algorithm; Image processing; Image restoration; Relevant information; Structure-priority; Textural information; Curves or lines (COLs); Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5139; https://ink.library.smu.edu.sg/context/sis_research/article/6142/viewcontent/09091579_pvoa.pdf
• Institution: Singapore Management University

With the significant increase in the use of image information, image restoration has been gaining much attention by researchers. Restoring the structural information as well as the textural information of a damaged image to produce visually plausible restorations is a challenging task. Genetic algorithm (GA) and its variants have been applied in many fields due to their global optimization capabilities. However, the applications of GA to the image restoration domain still remain an emerging discipline. It is still challenging and difficult to restore a damaged image by leveraging GA optimization. To address this problem, this paper proposes a novel GA-based image restoration method that can successfully restore a damaged image. We name it structure-priority image restoration through GA optimization. The main idea is to convert an image restoration task into an optimization problem, and to develop a GA optimization algorithm to solve it. In this study, the structural information of a damaged image, which is represented by curves or lines (COLs), is prioritized to be repaired first. The structural information is classified into relevant and irrelevant information according to the information of their locations. The relevant information is analyzed through the proposed GA optimization algorithm to find the matched COLs. The matched COLs are used to restore the structural information of the damaged area. The textural information will then be restored according to the different partitions separated by the restored structural information. Lastly, through case studies, we evaluate the proposed method by using four typical indices to measure the differences between the original and restored image. The results of case studies demonstrate the applicability and feasibility of the proposed method.