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Real-Time Video Super-Resolution with Spatio-Temporal Modeling and Redundancy-Aware Inference.
- Source :
-
Sensors (14248220) . Sep2023, Vol. 23 Issue 18, p7880. 20p. - Publication Year :
- 2023
-
Abstract
- 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, serving various purposes, such as video display and surveillance. This paper proposes a novel method for real-time video super-resolution. It effectively exploits spatial information by utilizing the capabilities of an image super-resolution model and leverages the temporal information inherent in videos. Specifically, the method incorporates a pre-trained image super-resolution network as its foundational framework, allowing it to leverage existing expertise for super-resolution. A fast temporal information aggregation module is presented to further aggregate temporal cues across frames. By using deformable convolution to align features of neighboring frames, this module takes advantage of inter-frame dependency. In addition, it employs a hierarchical fast spatial offset feature extraction and a channel attention-based temporal fusion. A redundancy-aware inference algorithm is developed to reduce computational redundancy by reusing intermediate features, achieving real-time inferring speed. Extensive experiments on several benchmarks demonstrate that the proposed method can reconstruct satisfactory results with strong quantitative performance and visual qualities. The real-time inferring ability makes it suitable for real-world deployment. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 23
- Issue :
- 18
- Database :
- Academic Search Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 172417744
- Full Text :
- https://doi.org/10.3390/s23187880