Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that
aims to obtain a high-resolution output from one of its low-resolution versions. Recently …

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 …

Image Super-Resolution (SR) is an important class of image processing techniqueso
enhance the resolution of images and videos in computer vision. Recent years have …

Most of the existing learning-based single image super-resolution (SISR) methods are
trained and evaluated on simulated datasets, where the low-resolution (LR) images are …

Video super-resolution (VSR) has become even more important recently to provide high
resolution (HR) contents for ultra high definition displays. While many deep learning based …

In this report we demonstrate that with same parameters and computational budgets, models
with wider features before ReLU activation have significantly better performance for single …

In this paper, we present a semi-supervised deep learning approach to accurately recover
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …

In this paper, we tackle the problem of blind image super-resolution (SR) with a reformulated
degradation model and two novel modules. Following the common practices of blind SR, our …

Recently, deep convolutional neural networks (CNNs) have been successfully applied to the
single-image super-resolution (SISR) task with great improvement in terms of both peak …

We demonstrate that a deep neural network can significantly improve optical microscopy,
enhancing its spatial resolution over a large field of view and depth of field. After its training …