Abstract Single Image Super-Resolution (SISR) is a crucial task in low-level computer vision
aiming to reconstruct high-resolution images from low-resolution counterparts. Conventional …

Transformer-based restoration methods achieve significant performance as the self-attention
(SA) of the Transformer can explore non-local information for better high-resolution image …

Transformers have exhibited promising performance in computer vision tasks including
image super-resolution (SR). However, popular transformer-based SR methods often …

Recently, remote sensing image super-resolution (RSISR) has drawn considerable attention
and made great breakthroughs based on convolutional neural networks (CNNs). Due to the …

With the intensification of urbanization in human society, pollen allergy has become a
seasonal epidemic disease with a considerable incidence rate, seriously affecting the …

Abstract Single Image Super-Resolution is a classic computer vision problem that involves
estimating high-resolution (HR) images from low-resolution (LR) ones. Although deep …

Omnidirectional images (ODIs) have become increasingly popular, as their large field-of-
view (FoV) can offer viewers the chance to freely choose the view directions in immersive …

It is well-known that image quality assessment usually meets with the problem of perception-
distortion (pd) tradeoff. The existing deep image super-resolution (SR) methods either focus …

Standard single-image super-resolution creates paired training data from high-resolution
images through fixed downsampling kernels. However, real-world super-resolution (RWSR) …

Blind image super-resolution (SR) aims to recover high-resolution (HR) images from low-
resolution (LR) inputs hindered by unknown degradation. Existing blind SR methods exploit …