Structure-Priority Image Restoration Through Genetic Algorithm Optimization

Abstract

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. INDEX TERMS Genetic algorithm, image processing, image restoration, relevant information, structurepriority, textural information, curves or lines (COLs). I. INTRODUCTION With the advent of social networking platforms, more and more people are sharing their thoughts about products or services by posting text messages, images, audio or video files [1]. Vision is the most advanced human sense [2], as a result, as social media data accumulates, it is not surprising that images play an important role and have become a more and more popular form of data on the Internet. With the significant increase in the use of images over the Internet, image processing and analysis have been gaining much research attention in the transportation, medical, aerospace, energy and other fields [3]-[6]. The associate editor coordinating the review of this manuscript and approving it for publication was Kathiravan Srinivasan .

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