Uhd Video Super-Resolution Using Low-Rank And Sparse Decomposition
| Autor: | Valia Guerra Ones, Salehe Erfanian Ebadi, Ebroul Izquierdo |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Matching (graph theory)
Computer science business.industry Frame (networking) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Sparse approximation 010501 environmental sciences 01 natural sciences Subpixel rendering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Neural coding Image resolution Group of pictures 0105 earth and related environmental sciences Block (data storage) |
| Zdroj: | ICCV Workshops 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) |
| DOI: | 10.5281/zenodo.1133264 |
| Popis: | Sparse coding-based algorithms have been successfully applied to the single-image super resolution problem. Conventional multi-image super-resolution (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 spatiotemporal information among the frames in the group of pictures (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. |
| Databáze: | OpenAIRE |
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