Recent studies have shown the importance of modeling long-range interactions in the
inpainting problem. To achieve this goal, existing approaches exploit either standalone …

Modern image inpainting systems, despite the significant progress, often struggle with large
missing areas, complex geometric structures, and high-resolution images. We find that one …

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

Image inpainting has been widely exploited in the field of computer vision and image
processing. The main purpose of image inpainting is to produce visually plausible structure …

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 …

To defend against manipulation of image content, such as splicing, copy-move, and
removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to …

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

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 …

Image Inpainting is an age-old image processing problem, with people from different eras
attempting to solve it using various methods. Traditional image inpainting algorithms have …

Existing inpainting methods have achieved promising performance in recovering defected
images of specific scenes. However, filling holes involving multiple semantic categories …