Image inpainting techniques have shown significant improvements by using deep neural
networks recently. However, most of them may either fail to reconstruct reasonable …

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

Image inpainting methods have shown significant improvements by using deep neural
networks recently. However, many of these techniques often create distorted structures or …

Conventional deep image inpainting methods are based on auto-encoder architecture, in
which the spatial details of images will be lost in the down-sampling process, leading to the …

Deep generative approaches have recently made considerable progress in image
inpainting by introducing structure priors. Due to the lack of proper interaction with image …

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 aims to restore the missing regions of corrupted images and make the
recovery result identical to the originally complete image, which is different from the common …

Image inpainting has made significant advances in recent years. However, it is still
challenging to recover corrupted images with both vivid textures and reasonable structures …

In this paper, we propose a generative multi-column network for image inpainting. This
network synthesizes different image components in a parallel manner within one stage. To …

This paper develops a multi-task learning framework that attempts to incorporate the image
structure knowledge to assist image inpainting, which is not well explored in previous works …