Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR)
image from a low-resolution (LR) observation, has been an active research topic in the area …

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with
focus on the proposed solutions and results. The challenge task was to super-resolve an …

Deep learning based approaches has achieved great performance in single image super-
resolution (SISR). However, recent advances in efficient super-resolution focus on reducing …

It is widely acknowledged that single image super-resolution (SISR) methods would not
perform well if the assumed degradation model deviates from those in real images. Although …

This work aims at designing a lightweight convolutional neural network for image super
resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective …

Blind image super-resolution (SR), aiming to super-resolve low-resolution images with
unknown degradation, has attracted increasing attention due to its significance in promoting …

Abstract Compressed Image Super-resolution has achieved great attention in recent years,
where images are degraded with compression artifacts and low-resolution artifacts. Since …

Efficient deep learning-based approaches have achieved remarkable performance in single
image super-resolution. However, recent studies on efficient super-resolution have mainly …

In recent years, we have witnessed the great advancement of Deep neural networks (DNNs)
in image restoration. However, a critical limitation is that they cannot generalize well to real …

Abstract Magnetic Resonance Imaging (MRI) typically comes at the cost of small spatial
coverage, high expenses and long scan times. Accelerating MRI acquisition by taking less …