Deep learning, eg, convolutional neural networks (CNNs), has achieved great success in
image processing and computer vision especially in high-level vision applications, such as …

Recently, convolutional neural network (CNN) has attracted tremendous attention and has
achieved great success in many image processing tasks. In this paper, we focus on CNN …

Through exploiting the image nonlocal self-similarity (NSS) prior by clustering similar
patches to construct patch groups, recent studies have revealed that structural sparse …

Compression artifacts arise in images whenever a lossy compression algorithm is applied.
These artifacts eliminate details present in the original image, or add noise and small …

Recent years have witnessed remarkable success of deep learning methods in quality
enhancement for compressed video. To better explore temporal information, existing …

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 …

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

Recent works that utilized deep models have achieved superior results in various image
restoration (IR) applications. Such approach is typically supervised, which requires a corpus …

Image restoration is a long-standing problem in image processing and low-level computer
vision. Recently, discriminative convolutional neural network (CNN)-based approaches …

When lossy video compression algorithms are applied, compression artifacts often appear in
videos, making decoded videos unpleasant for human visual systems. In this paper, we …