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 …

Image inpainting has been widely exploited in the field of computer vision and image
processing. The main purpose of image inpainting is to produce visually plausible structure …

To solving the problems that the existing image inpainting methods lack authenticity, do not
deal with the information of missing and non-missing regions flexibly, and do not deal with …

Recently data-driven image inpainting methods have made inspiring progress, impacting
fundamental image editing tasks such as object removal and damaged image repairing …

Existing inpainting methods have achieved promising performance in recovering defected
images of specific scenes. However, filling holes involving multiple semantic categories …

Abstract Domain knowledge of face shapes and structures plays an important role in face
inpainting. However, general inpainting methods focus mainly on the resolution of …

Completing a corrupted image with correct structures and reasonable textures for a mixed
scene remains an elusive challenge. Since the missing hole in a mixed scene of a corrupted …

Currently, most image inpainting algorithms based on deep learning cause information loss
when acquiring deep level features. It is not conducive to the image inpainting of texture …

We study the task of image inpainting, where an image with missing region is recovered with
plausible context. Recent approaches based on deep neural networks have exhibited …

Deep learning-based methods especially using convolutional neural network (CNN) and
generative adversarial network (GAN) have achieved certain success for the task of image …