Abstract
Image inpainting represents a sophisticated methodology within the domain of computer vision, whose core objective is to programmatically restore occluded regions or eliminate undesired elements from digital imagery. This process endeavors to reconstruct visual continuity such that the resulting image exhibits both perceptual naturalness and structural completeness. Image inpainting has gradually become a hot field in computer vision. It is used in film processing, watermark removal, photo processing, and other fields. Traditional image inpainting methods use adjacent pixels of the missing area for filling, which not only incur high computational costs but also suffer from ghost artifacts and blur. With the emergence of large-scale datasets, deep learning-based image inpainting methods have been successively proposed, significantly improving restoration quality. However, the current state-of-the-art methodologies continue to demonstrate suboptimal performance when confronted with images featuring extensive occluded domains. Additionally, technological advancements in related image fields bring new opportunities and challenges to image inpainting. This paper discusses three aspects: (1) a review of relevant datasets for image inpainting, (2) a detailed description and summary of state-of-the-art methods, and (3) an introduction of evaluation metrics with performance comparisons of representative approaches. Finally, we address existing challenges and future opportunities in this field.