Image inpainting is an ill-posed problem to recover missing or damaged image content
based on incomplete images with masks. Previous works usually predict the auxiliary …

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

Image inpainting task aims to recover missing pixels naturally and realistically. However,
previous deep learning approaches requires specific design for different types of masks and …

Significant progress has been made in image inpainting methods in recent years. However,
they are incapable of producing inpainting results with reasonable structures, rich detail, and …

Inpainting methods aim to restore missing parts of corrupted images and play a critical role
in many computer vision applications, such as object removal and image restoration …

The purpose of image inpainting is to recover scratches and damaged areas using context
information from remaining parts. In recent years, with the development of convolutional …

The latest deep learning-based approaches have shown promising results for the
challenging task of inpainting missing regions of an image. However, the existing methods …

Deep learning (DL) has demonstrated its powerful capabilities in the field of image
inpainting. The DL-based image inpainting approaches can produce visually plausible …

Deep autoencoder-based approaches have achieved significant improvements on restoring
damaged images, yet they still suffer from artifacts due to the inadequate representation and …

A novel continuous-mask-aware transformer for image inpainting, called CMT, is proposed
in this paper, which uses a continuous mask to represent the amounts of errors in tokens …