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 …

In this paper, we propose a new model called deep network cascade (DNC) to gradually
upscale low-resolution images layer by layer, each layer with a small scale factor. DNC is a …

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 super-resolution (SR) is a fundamental technique in the field of image processing and
computer vision. Recently, deep learning has witnessed remarkable progress in many super …

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 …

High-fidelity information, such as 4K quality videos and photographs, is increasing as high-
speed internet access becomes more widespread and less expensive. Even though camera …

Image super-resolution has become an important technology recently, especially in the
medical and industrial fields. As such, much effort has been given to develop image super …

Recently, it has been demonstrated that deep neural networks can significantly improve the
performance of single image super-resolution (SISR). Numerous studies have concentrated …

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 …

In recent years, deep learning methods have been successfully applied to single-image
super-resolution tasks. Despite their great performances, deep learning methods cannot be …