Single Image Deraining: A Comprehensive Benchmark Analysis
Siyuan Li, Iago Breno Araujo, Wenqi Ren, Zhangyang Wang, Eric K. Tokuda, Roberto Hirata Junior, Roberto Cesar-Junior, Jiawan Zhang
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.
BibTeX
@inproceedings{cvpr2019_singleimagederai,
title = {Single Image Deraining: A Comprehensive Benchmark Analysis},
author = {Siyuan Li and Iago Breno Araujo and Wenqi Ren and Zhangyang Wang and Eric K. Tokuda and Roberto Hirata Junior and Roberto Cesar-Junior and Jiawan Zhang and Xiaojie Guo and Xiaochun Cao},
booktitle = {CVPR 2019},
year = {2019}
}