Convolutional neural networks (CNN) have led to many state-of-the-art results spanning
through various fields. However, a clear and profound theoretical understanding of the …

Many image restoration algorithms in recent years are based on patch processing. The core
idea is to decompose the target image into fully overlapping patches, restore each of them …

This work considers noise removal from images, focusing on the well-known K-SVD
denoising algorithm. This sparsity-based method was proposed in 2006, and for a short …

This paper presents a multimodal image fusion method using a novel decomposition model
based on coupled dictionary learning. The proposed method is general and can be used for …

The non-local self-similarity property of natural images has been exploited extensively for
solving various image processing problems. When it comes to video sequences, harnessing …

Sparse representation with respect to an overcomplete dictionary is often used when
regularizing inverse problems in signal and image processing. In recent years, the …

The celebrated sparse representation model has led to remarkable results in various signal
processing tasks in the last decade. However, despite its initial purpose of serving as a …

In this paper we propose a generic recursive algorithm for improving image denoising
methods. Given the initial denoised image, we suggest repeating the following “SOS” …

We propose a fully convolutional neural-network architecture for image denoising which is
simple yet powerful. Its structure allows to exploit the gradual nature of the denoising …

Denoising and Super-Resolution are two inverse problems that have been extensively
studied. Over the years, these two tasks were treated as two distinct problems that deserve a …