Quanta Video Restoration

Prateek Chennuri, Yiheng Chi, Enze Jiang, GM Dilshan Godaliyadda, Abhiram Gnanasambandam, Hamid R Sheikh, Istvan Gyongy, Stanley H Chan

ECCV 2024

doi:10.1007/978-3-031-73661-2_9 /eccv/2024/chennuri2024eccv-quanta/

Abstract

The proliferation of single-photon image sensors has opened the door to a plethora of high-speed and low-light imaging applications. However, data collected by these sensors are often 1-bit or few-bit, and corrupted by noise and strong motion. Conventional video restoration methods are not designed to handle this situation, while specialized quanta burst algorithms have limited performance when the number of input frames is low. In this paper, we introduce Quanta Video Restoration (QUIVER), an end-to-end trainable network built on the core ideas of classical quanta restoration methods, i.e., pre-filtering, flow estimation, fusion, and refinement. We also collect and publish I2-2000FPS, a high-speed video dataset with the highest temporal resolution of 2000 frames-per-second, for training and testing. On simulated and real data, QUIVER outperforms existing quanta restoration methods by a significant margin. Code and dataset available at https://github.com/chennuriprateek/Quanta_Video_Restoration-QUIVER-

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Cite

Text

Chennuri et al. "Quanta Video Restoration." Proceedings of the European Conference on Computer Vision (ECCV), 2024. doi:10.1007/978-3-031-73661-2_9

Markdown

[Chennuri et al. "Quanta Video Restoration." Proceedings of the European Conference on Computer Vision (ECCV), 2024.](https://mlanthology.org/eccv/2024/chennuri2024eccv-quanta/) doi:10.1007/978-3-031-73661-2_9

BibTeX

@inproceedings{chennuri2024eccv-quanta,
  title     = {{Quanta Video Restoration}},
  author    = {Chennuri, Prateek and Chi, Yiheng and Jiang, Enze and Godaliyadda, GM Dilshan and Gnanasambandam, Abhiram and Sheikh, Hamid R and Gyongy, Istvan and Chan, Stanley H},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2024},
  doi       = {10.1007/978-3-031-73661-2_9},
  url       = {https://mlanthology.org/eccv/2024/chennuri2024eccv-quanta/}
}