期刊
INTERNATIONAL JOURNAL OF COMPUTER VISION
卷 129, 期 4, 页码 1301-1322出版社
SPRINGER
DOI: 10.1007/s11263-020-01416-w
关键词
Image deraining; Image quality assessment; Deep convolution network; Benchmark analysis
资金
- National Key R&D Program of China [2019YFB1406500]
- National Natural Science Foundation of China [61802403, U1605252, U1736219]
- Beijing Education Committee Cooperation Beijing Natural Science Foundation [KZ201910005007]
- Beijing Nova Program [Z201100006820074]
- Beijing Natural Science Foundation [L182057]
- Peng Cheng Laboratory Project of Guangdong Province [PCL2018KP004]
- Elite Scientist Sponsorship Program by the Beijing Association for Science and Technology
- CAPES
- CNPq
- Funding Agency FAPESP [15/22308-2]
This paper presents a study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark that includes images of various rain types. The assessment reveals a gap between the performance on synthetic rainy images and the practical demand on real-world images, indicating that image deraining is still a largely open problem despite recent advances.
The capability of image deraining is a highly desirable component of intelligent decision-making in autonomous driving and outdoor surveillance systems. Image deraining aims to restore the clean scene from the degraded image captured in a rainy day. Although numerous single image deraining algorithms have been recently proposed, these algorithms are mainly evaluated using certain type of synthetic images, assuming a specific rain model, plus a few real images. It remains unclear how these algorithms would perform on rainy images acquired in the wild and how we could gauge the progress in the field. This paper aims to bridge this gap. 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 of various rain types. This dataset highlights diverse rain models (rain streak, rain drop, rain and mist), as well as a rich variety of evaluation criteria (full- and no-reference objective, subjective, and task-specific). We further provide a comprehensive suite of criteria for deraining algorithm evaluation, including full- and no-reference metrics, subjective evaluation, and the novel task-driven evaluation. The proposed benchmark is accompanied with extensive experimental results that facilitate the assessment of the state-of-the-arts on a quantitative basis. Our evaluation and analysis indicate the gap between the achievable performance on synthetic rainy images and the practical demand on real-world images. We show that, despite many advances, image deraining is still a largely open problem. The paper is concluded by summarizing our general observations, identifying open research challenges and pointing out future directions. Our code and dataset is publicly available at .
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