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 convolutional networks (CNNs) have exhibited their potential in image inpainting for
producing plausible results. However, in most existing methods, eg, context encoder, the …

Image inpainting has made remarkable progress with recent advances in deep learning.
Popular networks mainly follow an encoder-decoder architecture (sometimes with skip …

Most convolutional network (CNN)-based inpainting methods adopt standard convolution to
indistinguishably treat valid pixels and holes, making them limited in handling irregular …

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

High-quality image inpainting requires filling missing regions in a damaged image with
plausible content. Existing works either fill the regions by copying high-resolution patches or …

Recently deep neural networks have achieved promising performance for filling large
missing regions in image inpainting tasks. They usually adopted the standard convolutional …

Existing inpainting methods have achieved promising performance for recovering regular or
small image defects. However, filling in large continuous holes remains difficult due to the …

Over the last few years, deep learning techniques have yielded significant improvements in
image inpainting. However, many of these techniques fail to reconstruct reasonable …

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 …