With the advances in scientific foundations and technological implementations, optical
metrology has become versatile problem-solving backbones in manufacturing, fundamental …

Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

Low-light image enhancement plays very important roles in low-level vision areas. Recent
works have built a great deal of deep learning models to address this task. However, these …

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

This paper introduces a novel large dataset for example-based single image super-
resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The …

We present an unsupervised visual feature learning algorithm driven by context-based pixel
prediction. By analogy with auto-encoders, we propose Context Encoders--a convolutional …

Abstract Model-based optimization methods and discriminative learning methods have been
the two dominant strategies for solving various inverse problems in low-level vision …

Deep neural networks (DNN) have achieved great success in image restoration. However,
most DNN methods are designed as a black box, lacking transparency and interpretability …

In this paper, we propose a very deep fully convolutional encoding-decoding framework for
image restoration such as denoising and super-resolution. The network is composed of …

This article is about a curious phenomenon. Suppose we have a data matrix, which is the
superposition of a low-rank component and a sparse component. Can we recover each …