In this article, we review recent uses of convolutional neural networks (CNNs) to solve
inverse problems in imaging. It has recently become feasible to train deep CNNs on large …

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

Recent years have witnessed the unprecedented success of deep convolutional neural
networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based …

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

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem.
However, CNNs depend on deeper network architectures to improve performance of image …

As a successful deep model applied in image super-resolution (SR), the Super-Resolution
Convolutional Neural Network (SRCNN)[1, 2] has demonstrated superior performance to the …

Traditionally, analytical methods have been used to solve imaging problems such as image
restoration, inpainting, and superresolution (SR). In recent years, the fields of machine and …

Recently, several models based on deep neural networks have achieved great success in
terms of both reconstruction accuracy and computational performance for single image …

In this paper, we address a rain removal problem from a single image, even in the presence
of heavy rain and rain streak accumulation. Our core ideas lie in our new rain image model …

We present recursive cascaded networks, a general architecture that enables learning deep
cascades, for deformable image registration. The proposed architecture is simple in design …