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

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 …

Recent state-of-the-art super-resolution methods have achieved impressive performance on
ideal datasets regardless of blur and noise. However, these methods always fail in real …

Image restoration is a critical task in low-level computer vision, aiming to restore high-quality
images from degraded inputs. Various models, such as convolutional neural networks …

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 …

Most of the recent literature on image super-resolution (SR) assumes the availability of
training data in the form of paired low resolution (LR) and high resolution (HR) images or the …

While single-image super-resolution (SISR) has attracted substantial interest in recent years,
the proposed approaches are limited to learning image priors in order to add high frequency …

In this paper, we take a new look at real-world image super-resolution (real-SR) from a multi-
task learning perspective. We demonstrate that the conventional formulation of real-SR can …

Most current super-resolution methods rely on low and high resolution image pairs to train a
network in a fully supervised manner. However, such image pairs are not available in real …