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, Xiaojie Guo, Xiaochun Cao; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3838-3847

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.

Related Material


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[bibtex]
@InProceedings{Li_2019_CVPR,
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},
title = {Single Image Deraining: A Comprehensive Benchmark Analysis},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}