This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on
the participating methods and final results. The challenge addresses the real world setting …

This paper reviews the NTIRE 2022 challenge on learning the super-Resolution space. This
challenge aims to raise awareness that the super-resolution problem is ill-posed. Since …

Super-resolution is an ill-posed problem, since it allows for multiple predictions for a given
low-resolution image. This fundamental fact is largely ignored by state-of-the-art deep …

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 …

Despite the breakthroughs in accuracy and speed of single image super-resolution using
faster and deeper convolutional neural networks, one central problem remains largely …

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

With the help of convolutional neural networks (CNN), the single image super-resolution
problem has been widely studied. Most of these CNN based methods focus on learning a …

We propose a deep learning method for single image super-resolution (SR). Our method
directly learns an end-to-end mapping between the low/high-resolution images. The …

The aim of single image super-resolution is to reconstruct a high-resolution image from a
single low-resolution input. Although the task is ill-posed it can be seen as finding a non …

In this paper we present seven techniques that everybody should know to improve example-
based single image super resolution (SR): 1) augmentation of data, 2) use of large …