Recently, convolutional networks have achieved remarkable development in remote
sensing image (RSI) super-resolution (SR) by minimizing the regression objectives, eg, MSE …

Optical-flow-based and kernel-based approaches have been extensively explored for
temporal compensation in satellite Video Super-Resolution (VSR). However, these …

Transformer-based method has demonstrated promising performance in image super-
resolution tasks, due to its long-range and global aggregation capability. However, the …

Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-
resolution (HR) using paired HR-LR training images. Conventional SR methods typically …

Hyperspectral videos (HSVs) play an important role in the monitoring domain, as they can
provide more information than red–green–blue (RGB) videos about the movement of …

Infrared small-target detection is a challenging task for deep learning-based methods,
because targets tend to disappear in the deep layers. To handle this problem, the existing …

Low-rank and sparse decomposition (LRSD)-based models are powerful and robust tools
for infrared small target detection. However, due to the calculation of singular value …

Global and local modeling is essential for image super-resolution tasks. However, current
efforts often lack explicit consideration of the cross-scale knowledge in large-scale earth …

Although recent video super-resolution (VSR) works show remarkable restoration
performance for low-resolution (LR) video downscaled by a fixed known blur kernel, blind …

Video super-resolution aims to generate high-resolution frames from low-resolution
counterparts. It can be regarded as a specialized application of image super-resolution …