Super resolution (SR) is an eminent system in the field of computer vison and image
processing to improve the visual perception of the poor-quality images. The key objective of …

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 …

Given a degraded input image, image restoration aims to recover the missing high-quality
image content. Numerous applications demand effective image restoration, eg …

With the goal of recovering high-quality image content from its degraded version, image
restoration enjoys numerous applications, such as in surveillance, computational …

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 …

This paper introduces a novel large dataset for video deblurring, video super-resolution and
studies the state-of-the-art as emerged from the NTIRE 2019 video restoration challenges …

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 …

Deep convolutional networks–based super-resolution is a fast-growing field with numerous
practical applications. In this exposition, we extensively compare more than 30 state-of-the …

Image quality assessment (IQA) is the key factor for the fast development of image
restoration (IR) algorithms. The most recent IR methods based on Generative Adversarial …

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 …