In recent years, with the rapid development of deep learning, super-resolution methods
based on convolutional neural networks (CNNs) have made great progress. However, the …

The learning-based methods for single image super-resolution (SISR) can reconstruct
realistic details, but they suffer severe performance degradation for low-light images …

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 …

We have witnessed great success of Single Image Super-Resolution (SISR) with
convolutional neural networks (CNNs) in recent years. However, most existing Super …

The effective utilization of hyperspectral image (HSI) and light detection and ranging
(LiDAR) data is essential for land cover classification. Recently, deep learning-based …

Recently, vision transformers have demonstrated their superiority against convolutional
neural networks (ConvNet) in various tasks including single-image super-resolution (SISR) …

Diffusion models have gained significant popularity for image-to-image translation tasks.
Previous efforts applying diffusion models to image super-resolution have demonstrated that …

As two practical and important image processing tasks, color demosaicking (CDM) and
super-resolution (SR) have been studied for decades. However, most literature studies …

Quality enhancement of HEVC compressed videos has attracted a lot of attentions in recent
years. In this article, we propose a robust multi-frame guided attention network (MGANet) to …