Abstract Non-Local Attention (NLA) brings significant improvement for Single Image Super-
Resolution (SISR) by leveraging intrinsic feature correlation in natural images. However …

Contrastive learning has achieved remarkable success on various high-level tasks, but there
are fewer contrastive learning-based methods proposed for low-level tasks. It is challenging …

Single image super-resolution, as a high dimensional structured prediction problem, aims to
characterize fine-grain information given a low-resolution sample. Recent advances in …

Recently, very deep convolutional neural networks (CNNs) have shown great power in
single image super-resolution (SISR) and achieved significant improvements against …

ConvNets can compete with transformers in high-level tasks by exploiting larger receptive
fields. To unleash the potential of ConvNet in super-resolution we propose a multi-scale …

Deep neural networks (DNNs) have witnessed remarkable achievement in image super-
resolution (SR), and plenty of DNN-based SR models with elaborated network designs have …

In recent years, constructing various deep convolutional neural networks (CNNs) for single-
image super-resolution (SISR) tasks has made significant progress. Despite their high …

Convolutional neural network (CNN) has achieved great success on image super-resolution
(SR). However, most deep CNN-based SR models take massive computations to obtain …

Image super-resolution (SR) is a fast-moving field with novel architectures attracting the
spotlight. However, most SR models were optimized with dated training strategies. In this …

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 …