Although image inpainting, or the art of repairing the old and deteriorated images, has been
around for many years, it has recently gained even more popularity, because of the recent …

In view of the faultiness that the existing image inpainting methods fail to make full use of the
complete region to predict the missing region features when the object features are seriously …

Image inpainting that completes large free-form missing regions in images is a promising yet
challenging task. State-of-the-art approaches have achieved significant progress by taking …

In the last few years, image inpainting methods based on deep learning models had shown
obvious advantages compared with existing traditional methods. The former can better …

The most image inpainting algorithms often have existed problems such as blurred image,
texture distortion and semantic inaccuracy, and the image inpainting effect is limited for …

Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity
models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing …

Group sparse representation (GSR) has made great strides in image restoration producing
superior performance, realized through employing a powerful mechanism to integrate the …

Deep learning (DL) has demonstrated its powerful capabilities in the field of image
inpainting, which could produce visually plausible results. Meanwhile, the malicious use of …

Sparse representation has achieved great success in various image processing and
computer vision tasks. For image processing, typical patch-based sparse representation …

As a spotlighted nonlocal image representation model, group sparse representation (GSR)
has demonstrated a great potential in diverse image restoration tasks. Most of the existing …