Deep Learning-based Image and Video Inpainting: A Survey
It synthesizes existing research for researchers and practitioners in computer vision and graphics, but is incremental as it does not introduce new methods.
This paper provides a comprehensive survey of deep learning-based methods for image and video inpainting, reviewing architectures, training objectives, datasets, and performance evaluations to summarize progress and identify challenges in the field.
Image and video inpainting is a classic problem in computer vision and computer graphics, aiming to fill in the plausible and realistic content in the missing areas of images and videos. With the advance of deep learning, this problem has achieved significant progress recently. The goal of this paper is to comprehensively review the deep learning-based methods for image and video inpainting. Specifically, we sort existing methods into different categories from the perspective of their high-level inpainting pipeline, present different deep learning architectures, including CNN, VAE, GAN, diffusion models, etc., and summarize techniques for module design. We review the training objectives and the common benchmark datasets. We present evaluation metrics for low-level pixel and high-level perceptional similarity, conduct a performance evaluation, and discuss the strengths and weaknesses of representative inpainting methods. We also discuss related real-world applications. Finally, we discuss open challenges and suggest potential future research directions.