The discriminative model learning for image denoising has been recently attracting
considerable attentions due to its favorable denoising performance. In this paper, we take …

The recent advances in hardware and imaging systems made the digital cameras
ubiquitous. Although the development of hardware has steadily improved the quality of …

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

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks
toward solving image denoising, existing methods mostly rely on simple noise assumptions …

Much of the recent research on solving iterative inference problems focuses on moving
away from hand-chosen inference algorithms and towards learned inference. In the latter …

Image denoising is still a challenging problem in image processing. The authors propose a
novel image denoising method based on a deep convolution neural network (DCNN) …

Recent works on structural sparse representation (SSR), which exploit image nonlocal self-
similarity (NSS) prior by grouping similar patches for processing, have demonstrated …

We propose a data-dependent denoising procedure to restore noisy images. Different from
existing denoising algorithms which search for patches from either the noisy image or a …

Deep learning has been successfully applied to image super resolution (SR). In this paper,
we propose a deep joint super resolution (DJSR) model to exploit both external and self …

Single image super-resolution (SR) aims to estimate a high-resolution (HR) image from a
low-resolution (LR) input. Image priors are commonly learned to regularize the, otherwise …