Image restoration is a long-standing problem in low-level computer vision with many
interesting applications. We describe a flexible learning framework based on the concept of …

Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and
theory that have been accumulated over the years. Recently, this field has been immensely …

Given an image, we wish to produce an image of larger size with significantly more pixels
and higher image quality. This is generally known as the single image super-resolution …

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 …

In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …

Sparse coding has been widely applied to learning-based single image super-resolution
(SR) and has obtained promising performance by jointly learning effective representations …

We consider the single image super-resolution (SISR) problem, where a high-resolution
(HR) image is generated based on a low-resolution (LR) input. Recently, generative …

The recently proposed multilayer convolutional sparse coding (ML-CSC) model, consisting
of a cascade of convolutional sparse layers, provides a new interpretation of convolutional …

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” …