Structured Sparsity Learning for Efficient Video Super-Resolution
Author:
Affiliation:
1. Tsinghua University
2. Shanghai AI Laboratory
3. ETH Zürich
4. ByteDance Inc
5. University of Texas at Dallas
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10203037/10203050/10203790.pdf?arnumber=10203790
Reference58 articles.
1. Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
2. BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
3. BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
4. Recent advances in efficient computation of deep convolutional neural networks
5. Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges
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