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

Video super-resolution (SR) aims to reconstruct the corresponding high-resolution (HR)
frames from consecutive low-resolution (LR) frames. It is crucial for video SR to harness both …

The high computational costs of video super-resolution (VSR) models hinder their
deployment on resource-limited devices, eg, smartphones and drones. Existing VSR models …

Spatial-Temporal Video Super-Resolution (ST-VSR) aims to generate high-quality videos
with higher resolution (HR) and higher frame rate (HFR). Quite intuitively, pioneering two …

As a new earth observation tool, satellite video has been widely used in remote-sensing
field for dynamic analysis. Video super-resolution (VSR) technique has thus attracted …

How to effectively fuse temporal information from consecutive frames remains to be a non-
trivial problem in video super-resolution (SR), since most existing fusion strategies (direct …

Recently, deep learning based video super-resolution (SR) methods have achieved
promising performance. To simultaneously exploit the spatial and temporal information of …

Video super-resolution (VSR) aims to restore a sequence of high-resolution (HR) frames
from their low-resolution (LR) counterparts. Although some progress has been made, there …

Recent years have seen considerable research activities devoted to video enhancement
that simultaneously increases temporal frame rate and spatial resolution. However, the …

Video super-resolution (VSR) aims to improve the spatial resolution of low-resolution (LR)
videos. Existing VSR methods are mostly trained and evaluated on synthetic datasets …