Towards Source-Based Classification of Image Inpainting Techniques: A Survey

I. J. Sreelakshmy () and C. Kovoor Binsu ()
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I. J. Sreelakshmy: Cochin University of Science and Technology, Kerala, India
C. Kovoor Binsu: Cochin University of Science and Technology, Kerala, India

Journal of Information & Knowledge Management (JIKM), 2021, vol. 20, issue 03, 1-39

Abstract: Image inpainting is a process of reconstructing an incomplete image from the available information in a visually plausible way. In the proposed framework, existing image inpainting methods are classified in a new perspective. The information which is referred to, while reconstructing an image, is a critical factor of inpainting algorithms. Source of this information can be host image itself or an external source. The proposed framework broadly classifies inpainting algorithms into introspective and extrospective categories based on the source of information. Various parameters influencing the algorithms under these categories are identified in the proposed framework. A comprehensive list of all publicly available datasets along with the references are also summarized. Additionally, an in-depth analysis of the results obtained with the surveyed techniques is performed based on quantitative and qualitative parameters. The proposed framework aids the user in identifying the most suitable algorithm for various inpainting scenarios.

Keywords: Image inpainting; occlusion removal; exemplar; learning based; image restoration (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0219649221500398

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