Boundary precedence image inpainting method based on self-organizing maps

• Main Authors: PEN, Haibo, WANG, Quan, WANG, Zhaoxia
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Image inpainting; Layer separation; Self-organizing Maps (SOMs); Boundary precedence (BP); Databases and Information Systems; Data Storage Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6211; https://ink.library.smu.edu.sg/context/sis_research/article/7214/viewcontent/2021_Boundary_precedence_image_inpainting_method_based_on_Self_organizing_Maps.pdf
• Institution: Singapore Management University

In addition to text data analysis, image analysis is an area that has increasingly gained importance in recent years because more and more image data have spread throughout the internet and real life. As an important segment of image analysis techniques, image restoration has been attracting a lot of researchers’ attention. As one of AI methodologies, Self-organizing Maps (SOMs) have been applied to a great number of useful applications. However, it has rarely been applied to the domain of image restoration. In this paper, we propose a novel image restoration method by leveraging the capability of SOMs, and we name it “boundary precedence image inpainting method based on SOMs”. In the proposed method, SOMs are used to separate a damaged image into different layers according to the pixel information of the image. Each pixel in the damaged area is considered to be a center of a square area, which is called a waiting-for-inpainting patch. The waiting-for-inpainting patch filling order is calculated by the boundary precedence method in which the information of the separated image layers obtained by SOMs is analyzed and used to calculate the filling order. According to the proposed method, the waiting-for-inpainting patches on the boundaries of the damaged region are restored first and the filling order of this proposed method depends on the precedence values of each waiting-for-inpainting patch. Case studies demonstrate the effectiveness of this proposed method. Both textural and structural information can be nicely repaired by the proposed method.