Single Image Deraining: A Comprehensive Benchmark Analysis
Abstract
We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images. This dataset highlights diverse data sources and image contents, and is divided into three subsets (rain streak, rain drop, rain and mist), each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation. Experiments on the dataset shed light on the comparisons and limitations of state-of-the-art deraining algorithms, and suggest promising future directions.
Cite
Text
Li et al. "Single Image Deraining: A Comprehensive Benchmark Analysis." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00396Markdown
[Li et al. "Single Image Deraining: A Comprehensive Benchmark Analysis." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/li2019cvpr-single/) doi:10.1109/CVPR.2019.00396BibTeX
@inproceedings{li2019cvpr-single,
title = {{Single Image Deraining: A Comprehensive Benchmark Analysis}},
author = {Li, Siyuan and Araujo, Iago Breno and Ren, Wenqi and Wang, Zhangyang and Tokuda, Eric K. and Junior, Roberto Hirata and Cesar-Junior, Roberto and Zhang, Jiawan and Guo, Xiaojie and Cao, Xiaochun},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2019},
doi = {10.1109/CVPR.2019.00396},
url = {https://mlanthology.org/cvpr/2019/li2019cvpr-single/}
}