EDVR: Video Restoration With Enhanced Deformable Convolutional Networks
Chinese University of Hong Kong · Nanyang Technological University · +1 more institution
Abstract
Video restoration tasks, including super-resolution, deblurring, etc, are drawing increasing attention in the computer vision community. A challenging benchmark named REDS is released in the NTIRE19 Challenge. This new benchmark challenges existing methods from two aspects: (1) how to align multiple frames given large motions, and (2) how to effectively fuse different frames with diverse motion and blur. In this work, we propose a novel Video Restoration framework with Enhanced Deformable convolutions, termed EDVR, to address these challenges. First, to handle large motions, we devise a Pyramid, Cascading and Deformable (PCD) alignment module, in which frame alignment is done at the feature level using…
Citation impact
- FWCI
- 57.97
- Percentile
- 100%
- References
- 67
Authors
5Topics & keywords
- Deblurring
- Computer science
- Benchmark (surveying)
- Artificial intelligence
- Margin (machine learning)
- Fuse (electrical)
- Computer vision
- Image restoration
- Sustainable cities and communities