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

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 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 focus on image inpainting task, aiming at recovering the missing area of an
incomplete image given the context information. Recent development in deep generative …

Image inpainting techniques have shown promising improvement with the assistance of
generative adversarial networks (GANs) recently. However, most of them often suffered from …

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

Deep encoder-decoder based CNNs have advanced image inpainting methods for hole
filling. While existing methods recover structures and textures step-by-step in the hole …

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

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 …

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 …