학술논문

Local-Global Temporal Difference Learning for Satellite Video Super-Resolution
Document Type
Periodical
Source
IEEE Transactions on Circuits and Systems for Video Technology IEEE Trans. Circuits Syst. Video Technol. Circuits and Systems for Video Technology, IEEE Transactions on. 34(4):2789-2802 Apr, 2024
Subject
Components, Circuits, Devices and Systems
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Satellites
Optical flow
Convolution
Estimation
Superresolution
Remote sensing
Three-dimensional displays
Satellite video
super-resolution
temporal difference
local-global compensation
remote sensing
Language
ISSN
1051-8215
1558-2205
Abstract
Optical-flow-based and kernel-based approaches have been extensively explored for temporal compensation in satellite Video Super-Resolution (VSR). However, these techniques are less generalized in large-scale or complex scenarios, especially in satellite videos. In this paper, we propose to exploit the well-defined temporal difference for efficient and effective temporal compensation. To fully utilize the local and global temporal information within frames, we systematically modeled the short-term and long-term temporal discrepancies since we observe that these discrepancies offer distinct and mutually complementary properties. Specifically, we devise a Short-term Temporal Difference Module (S-TDM) to extract local motion representations from RGB difference maps between adjacent frames, which yields more clues for accurate texture representation. To explore the global dependency in the entire frame sequence, a Long-term Temporal Difference Module (L-TDM) is proposed, where the differences between forward and backward segments are incorporated and activated to guide the modulation of the temporal feature, leading to a holistic global compensation. Moreover, we further propose a Difference Compensation Unit (DCU) to enrich the interaction between the spatial distribution of the target frame and temporal compensated results, which helps maintain spatial consistency while refining the features to avoid misalignment. Rigorous objective and subjective evaluations conducted across five mainstream video satellites demonstrate that our method performs favorably against state-of-the-art approaches. Code will be available at https://github.com/XY-boy/LGTD.