Rank-One Prior: Toward Real-Time Scene Recovery

Abstract

Scene recovery is a fundamental imaging task for several practical applications, e.g., video surveillance and autonomous vehicles, etc. To improve visual quality under different weather/imaging conditions, we propose a real-time light correction method to recover the degraded scenes in the cases of sandstorms, underwater, and haze. The heart of our work is that we propose an intensity projection strategy to estimate the transmission. This strategy is motivated by a straightforward rank-one transmission prior. The complexity of transmission estimation is O(N) where N is the size of the single image. Then we can recover the scene in real-time. Comprehensive experiments on different types of weather/imaging conditions illustrate that our method outperforms competitively several state-of-the-art imaging methods in terms of efficiency and robustness.

Cite

Text

Liu et al. "Rank-One Prior: Toward Real-Time Scene Recovery." Conference on Computer Vision and Pattern Recognition, 2021. doi:10.1109/CVPR46437.2021.01456

Markdown

[Liu et al. "Rank-One Prior: Toward Real-Time Scene Recovery." Conference on Computer Vision and Pattern Recognition, 2021.](https://mlanthology.org/cvpr/2021/liu2021cvpr-rankone/) doi:10.1109/CVPR46437.2021.01456

BibTeX

@inproceedings{liu2021cvpr-rankone,
  title     = {{Rank-One Prior: Toward Real-Time Scene Recovery}},
  author    = {Liu, Jun and Liu, Wen and Sun, Jianing and Zeng, Tieyong},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2021},
  pages     = {14802-14810},
  doi       = {10.1109/CVPR46437.2021.01456},
  url       = {https://mlanthology.org/cvpr/2021/liu2021cvpr-rankone/}
}