computer-vision
Multi-Frame Super-Resolution
3260 papers • 126 benchmarks • 313 datasets
When multiple images of the same view are taken from slightly different positions, perhaps also at different times, then they collectively contain more information than any single image on its own. Multi-Frame Super-Resolution fuses these low-res inputs into a composite high-res image that can reveal some of the original detail that cannot be recovered from any low-res image alone. ( Credit: HighRes-net )
(Image credit: Papersgraph)
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Most implemented papers
Wide Activation for Efficient and Accurate Image Super-Resolution
Jianchao Yang, Thomas S. Huang, Xinchao Wang, Zhaowen Wang, Yuchen Fan, N. Xu, Jiahui Yu•Sun Aug 26 2018
This report demonstrates that with same parameters and computational budgets, models with wider features before ReLU activation have significantly better performance for single image super-resolution (SISR) and introduces linear low-rank convolution into SR networks to achieve even better accuracy-efficiency tradeoffs.
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