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 …

Single-image super-resolution (SISR) is an important task in image processing, which aims
to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and …

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 …

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 …

Numerous single image super-resolution (SISR) algorithms have been proposed during the
past years to reconstruct a high-resolution (HR) image from its low-resolution (LR) …

Image super‐resolution reconstruction refers to a technique of recovering a high‐resolution
(HR) image (or multiple images) from a low‐resolution (LR) degraded image (or multiple …

Image super-resolution (SR) is one of the vital image processing methods that improve the
resolution of an image in the field of computer vision. In the last two decades, significant …

Learning-based approaches for image super-resolution (SR) have attracted the attention
from researchers in the past few years. In this paper, we present a novel self-learning …

These days, unsupervised super-resolution (SR) is soaring due to its practical and
promising potential in real scenarios. The philosophy of off-the-shelf approaches lies in the …

Conventional supervised super-resolution (SR) approaches are trained with massive
external SR datasets but fail to exploit desirable properties of the given test image. On the …