| Full text | |
| Author(s): |
Li, Siyuan
[1]
;
Ren, Wenqi
[2]
;
Wang, Feng
[1]
;
Araujo, Iago Breno
[3]
;
Tokuda, Eric K.
[3]
;
Junior, Roberto Hirata
[3]
;
Cesar-Jr., Roberto M.
;
Wang, Zhangyang
[4]
;
Cao, Xiaochun
[2, 5, 6]
Total Authors: 9
|
| Affiliation: | [1] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350 - Peoples R China
[2] Chinese Acad Sci, Inst Informat Engn, Beijing 100093 - Peoples R China
[3] Inst Math & Stat IME, Sao Paulo - Brazil
[4] Univ Texas Austin, Dept Elect & Comp Engn, Austin, TX 78712 - USA
[5] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100049 - Peoples R China
[6] Peng Cheng Lab, Cyberspace Secur Res Ctr, Shenzhen 518055 - Peoples R China
Total Affiliations: 6
|
| Document type: | Journal article |
| Source: | INTERNATIONAL JOURNAL OF COMPUTER VISION; v. 129, n. 4, p. 1301-1322, APR 2021. |
| Web of Science Citations: | 10 |
| Abstract | |
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 . (AU) | |
| FAPESP's process: | 15/22308-2 - Intermediate representations in Computational Science for knowledge discovery |
| Grantee: | Roberto Marcondes Cesar Junior |
| Support Opportunities: | Research Projects - Thematic Grants |