Image inpainting is an important research direction in the study of computer vision, and is
widely used in image editing and photo inpainting etc. Traditional image inpainting …

Images can be described as visual representations or likeness of something (person or
object) which can be reproduced or captured, eg a hand drawing, photographic material …

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

Inpainting arbitrary missing regions is challenging because learning valid features for
various masked regions is nontrivial. Though U-shaped encoder-decoder frameworks have …

Text-to-image generation has achieved astonishing results yet precise spatial controllability
and prompt fidelity remain highly challenging. This limitation is typically addressed through …

Nonlocal texture similarity and local intensity smoothness are both essential for solving most
image inpainting problems. In this paper, we propose a novel image inpainting algorithm …

To recover the corrupted pixels, traditional inpainting methods based on low-rank priors
generally need to solve a convex optimization problem by an iterative singular value …

In order to solve the issue mismatching and structure disconnecting in exemplar-based
image inpainting, an image completion algorithm based on improved total variation …

Image inpainting, image deblurring, and noise removal are influential concepts in the field of
digital image processing. Second-order diffusion-based image restoration models suffer …

Benefiting from powerful convolutional neural networks (CNNs), learning-based image
inpainting methods have made significant breakthroughs over the years. However, some …