Convolutional neural networks based single-image superresolution (SISR) has made great
progress in recent years. However, it is difficult to apply these methods to real-world …

Stereo image super-resolution exploits additional features from cross view image pairs for
high resolution (HR) image reconstruction. Recently, several new methods have been …

Low-light image enhancement based on deep convolutional neural networks (CNNs) has
revealed prominent performance in recent years. However, it is still a challenging task since …

Deep learning methods have shown outstanding performance in many applications,
including single-image super-resolution (SISR). With residual connection architecture …

Abstract Recently, super-resolution (SR) techniques based on deep learning have taken
more and more attention, aiming to improve the images and videos resolutions. Most of the …

Convolutional neural networks have been proven to be of great benefit for single-image
super-resolution (SISR). However, previous works do not make full use of multi-scale …

It is challenging to restore low-resolution (LR) images to super-resolution (SR) images with
correct and clear details. Existing deep learning works almost neglect the inherent structural …

Local and non-local attention-based methods have been well studied in various image
restoration tasks while leading to promising performance. However, most of the existing …

Light field (LF) cameras, which can record real-word scenes from multiple viewpoints in a
single shot, are widely used in 3D reconstruction, re-focusing, and virtual reality etc …

How to generate the ground-truth (GT) image is a critical issue for training realistic image
super-resolution (Real-ISR) models. Existing methods mostly take a set of high-resolution …