UHD Video Super-Resolution Using Low-Rank and Sparse Decomposition
- 1. Queen Mary University of London
Description
Sparse coding-based algorithms have been successfully applied to the single-image super resolution problem. Conventional multi-image SR algorithms incorporate auxiliary frames into the model by a registration process using subpixel block matching algorithms that are computationally expensive. This becomes increasingly important as super-resolving UHD video content with existing sparse-based SR approaches become less efficient. In order to fully utilize the spatio-temporal information, we propose a novel multi-frame video SR approach that is aided by a low-rank plus sparse decomposition of the video sequence. We introduce a group of pictures structure where we seek a rank-1 low-rank part that recovers the shared spatio-temporal information among the frames in the GOP. Then we super-resolve the low-rank frame and sparse frames separately. This assumption results in significant time reductions, as well as surpassing state-of-the-art performance both qualitatively and quantitatively.
Files
Ebadi_UHD_Video_Super-Resolution_ICCV_2017_paper.pdf
Files
(5.7 MB)
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