Abstract
Image inpainting is an important research direction in the study of computer vision, and is widely used in image editing and photo inpainting etc. Traditional image inpainting algorithms are often difficult to deal with large-scale image deletion, since these algorithms are prone to inconsistent image semantics. With the rapid development of deep learning (DL) in recent years, the advantages of DL in image processing have become increasingly prominent, it can solve the problems existing in traditional image inpainting algorithms to a certain extent. At present, image inpainting based on deep learning becomes a research hotspot in computer vision. In this article, we systematically summarize and analyze the literature on image inpainting based on deep learning. First, we review the specific research status of deep learning technology in the field of image inpainting in the past 15 years; then, We deeply study and analyze the existing image restoration methods based on different neural network structures and their information fusion methods. In addition, we also classify and summarize the different tasks of image inpainting according to the application scenarios of image inpainting. Finally, we point out some problems that urgently need to be solved for deep learning in the field of image inpainting, provide constructive suggestions and discuss the future development direction.
Published Version
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