Recent advances in deep learning have shown exciting promise in filling large holes in
natural images with semantically plausible and context aware details, impacting …

Recent deep learning based approaches have shown promising results for the challenging
task of inpainting large missing regions in an image. These methods can generate visually …

Patch-based methods and deep networks have been employed to tackle image inpainting
problem, with their own strengths and weaknesses. Patch-based methods are capable of …

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 …

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

Recent image inpainting methods have made great progress but often struggle to generate
plausible image structures when dealing with large holes in complex images. This is …

Deep image inpainting has made impressive progress with recent advances in image
generation and processing algorithms. We claim that the performance of inpainting …

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

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

Image inpainting aims to generate realistic content for missing regions of an image. Existing
methods often struggle to produce visually coherent content for missing regions of an image …