Deep Burst Super-Resolution
Goutam Bhat, Martin Danelljan, Luc Van Gool, Radu Timofte
2021Year
24Top-tier citations
Abstract
RAW Input burst Single Image SR Burst SR (Ours) HR Reference Figure 1. Our network generates a super-resolved RGB image from an input burst consisting of multiple noisy RAW frames. In contrast to the single image baseline, our approach combines information from multiple frames to obtain a more detailed reconstruction of the scene. The results shown are for super-resolution by a factor of 4.
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Install the CLIlune papers fulltext d2f9d97f-07cc-43f9-ab88-8146fc1c375fCited by top-tier papers24
- Burst Image Restoration and EnhancementAkshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan et al.CVPR 2022 · 99 citations
- Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingGoutam Bhat, Martin Danelljan, Fisher Yu, Luc Van Gool et al.ICCV 2021 · 77 citations
- Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image BurstsBruno Lecouat, Jean Ponce, Julien MairalICCV 2021 · 46 citations
- Towards Real-World Burst Image Super-Resolution: Benchmark and MethodPengxu Wei, Yujing Sun, Xingbei Guo, Chang Liu et al.ICCV 2023 · 31 citations
- Self-Supervised Super-Resolution for Multi-Exposure Push-Frame SatellitesNgoc Long Nguyen, Jérémy Anger, Axel Davy, Pablo Arias et al.CVPR 2022 · 21 citations
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