Satellite imageries are an important geoinformation source for different applications in the
Earth Science field. However, due to the limitation of the optic and sensor technologies and …

Video super-resolution (VSR) is reconstructing high-resolution videos from low resolution
ones. Recently, the VSR methods based on deep neural networks have made great …

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

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

Pan-sharpening aims to integrate the complementary information of texture-rich PAN images
and multi-spectral (MS) images to produce the texture-rich MS images. Despite the …

The current superresolution (SR) methods based on deep learning have shown remarkable
comparative advantages but remain unsatisfactory in recovering the high-frequency edge …

Deep Learning, and Deep Neural Networks in particular, have established themselves as
the new norm in signal and data processing, achieving state-of-the-art performance in …

Recent efforts have witnessed remarkable progress in satellite video super-resolution
(SVSR). However, most SVSR methods usually assume the degradation is fixed and known …

Video super-resolution (SR) is focused on reconstructing high-resolution frames from
consecutive low-resolution (LR) frames. Most previous video SR methods based on …

Convolutional network compression methods require training data for achieving acceptable
results, but training data is routinely unavailable due to some privacy and transmission …