A Generalized Low-Rank Appearance Model for Spatio-Temporally Correlated Rain Streaks
Abstract
In this paper, we propose a novel low-rank appearance model for removing rain streaks. Different from previous work, our method needs neither rain pixel detection nor time-consuming dictionary learning stage. Instead, as rain streaks usually reveal similar and repeated patterns on imaging scene, we propose and generalize a low-rank model from matrix to tensor structure in order to capture the spatio-temporally correlated rain streaks. With the appearance model, we thus remove rain streaks from image/video (and also other high-order image structure) in a unified way. Our experimental results demonstrate competitive (or even better) visual quality and efficient run-time in comparison with state of the art.
Cite
Text
Chen and Hsu. "A Generalized Low-Rank Appearance Model for Spatio-Temporally Correlated Rain Streaks." International Conference on Computer Vision, 2013. doi:10.1109/ICCV.2013.247Markdown
[Chen and Hsu. "A Generalized Low-Rank Appearance Model for Spatio-Temporally Correlated Rain Streaks." International Conference on Computer Vision, 2013.](https://mlanthology.org/iccv/2013/chen2013iccv-generalized/) doi:10.1109/ICCV.2013.247BibTeX
@inproceedings{chen2013iccv-generalized,
title = {{A Generalized Low-Rank Appearance Model for Spatio-Temporally Correlated Rain Streaks}},
author = {Chen, Yi-Lei and Hsu, Chiou-Ting},
booktitle = {International Conference on Computer Vision},
year = {2013},
doi = {10.1109/ICCV.2013.247},
url = {https://mlanthology.org/iccv/2013/chen2013iccv-generalized/}
}