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

Given an incomplete image without additional constraint, image inpainting natively allows
for multiple solutions as long as they appear plausible. Recently, multiple-solution inpainting …

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 …

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

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

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

Recent deep generative inpainting methods use attention layers to allow the generator to
explicitly borrow feature patches from the known region to complete a missing region. Due to …

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

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

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 …