Raindrop detection and removal using salient visual features
Abstract
Raindrops on vehicles' windshields can degrade the performance of in-vehicle vision systems. In this paper, we present a novel approach that detects and removes raindrops in the captured image when using a single in-vehicle camera. When driving in light or moderate rainy conditions, raindrops appear as small circlets on the windshield in each image frame. Therefore, by analyzing the color, texture and shape characteristics of raindrops in images, we first identify possible raindrop candidates in the regions of interest (ROI), which are small locally salient droplets in a raindrop saliency map. Then, a learning-based verification algorithm is proposed to reduce the number of false alarms (i.e., clear regions mis-detected as raindrops). Finally, we fill in the regions occupied by the raindrops using image inpainting techniques. Numerical experiments indicate that the proposed method is capable of detecting and reducing raindrops in various rain and road scenarios. We also quantify the improvement offered by the proposed method over the state-of-the-art algorithms aimed at the same problem and the benefits to the in-vehicle vision applications like clear path detection.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Wu et al.'s proposed algorithm successfully removed raindrops from images of the windshield at slow driving speed but did not perform as well at higher speeds, due to increased raindrop shape deviation from the assumed circular shape.
“The Proposed algorithm also successfully removed raindrops from images of the windshield at slow driving speed but did not perform as well at higher speeds, due to increased raindrop shape deviation from the assumed circular shape.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [35]
Wu et al.'s proposed saliency-map algorithm performed better than Kurihata et al. [18] Eigendrops and Roser and Geiger [21] initial raindrop detection stage, both in terms of Precision and Recall metrics.
“Results showed that the proposed algorithm performed better than the other two, both in terms of Precision and Recall metrics.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [35]
Cites
Use of the hough transformation to detect lines and curves in pictures
Duda, R. O., & Hart, P. E. (1972) · Communications of the ACM · doi:10.1145/361237.361242 · Cited by 6,633 · see citing works
Bertalmı́o, M., Sapiro, G., Caselles, V., & Ballester, C. (2000) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/344779.344972 · Cited by 3,747 · see citing works
Probability Estimates for Multi-class Classification by Pairwise Coupling
Wu, T., Lin, C., & Weng, R. C. (2004) · Journal of Machine Learning Research · doi:10.5555/1005332.1016791 · Cited by 1,673 · see citing works
Modeling the role of salience in the allocation of overt visual attention
Parkhurst, D. J., Law, K., & Niebur, E. (2002) · Vision Research · doi:10.1016/s0042-6989(01)00250-4 · Cited by 1,390 · see citing works
Probability Estimates for Multi-class Classification by Pairwise Coupling
Wu, T., Lin, C., Weng, R. C., & Singer, Y. (2003) · Cited by 989 · see citing works
Detection and removal of rain from videos
Garg, K., & Nayar, S. K. (2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315077 · Cited by 531 · see citing works
Rainy weather recognition from in-vehicle camera images for driver assistance
Kurihata, H., Takahashi, T., Ide, I., Mekada, Y., Murase, H., Tamatsu, Y., & Miyahara, T. (2005) · IEEE Proceedings. Intelligent Vehicles Symposium, 2005. · doi:10.1109/ivs.2005.1505103 · Cited by 177 · see citing works
Video-based raindrop detection for improved image registration
Roser, M., & Geiger, A. (2009) · 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops · doi:10.1109/iccvw.2009.5457650 · Cited by 126 · see citing works
A virtual wiper - restoration of deteriorated images by using multiple cameras
Yamashita, A., Kuramoto, M., Kaneko, T., & Miura, K. T. (2004) · Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453) · doi:10.1109/iros.2003.1249637 · Cited by 32 · see citing works
Removal of adherent noises from image sequences by spatio-temporal image processing
Yamashita, A., Fukuchi, I., Kaneko, T., & Miura, K. T. (2008) · 2008 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2008.4543570 · Cited by 21 · see citing works
Camera-based clear path detection
Wu, Q., Zhang, W., Chen, T., et al. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5495356 · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
A Review of Detection and Removal of Raindrops in Automotive Vision Systems
Hamzeh, Y., & Rawashdeh, S. A. (2021) · Journal of Imaging · doi:10.3390/jimaging7030052 · Cited by 21 · see citing works
“[35] Use the inpainting technique through smooth propagation in the equal intensity line direction.”
BackgroundMethodInfluential
- Authors
- Wu, Q., Zhang, W., & Vijaya Kumar, B. V. K.
- Affiliations
- Carnegie Mellon University (US)
- General Motors (United States) (US)
- ORCID
- Venue
- 2012 19th IEEE International Conference on Image Processing
- Publisher
- IEEE
- Published
- 2012-09
- Pages
- 941-944
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W1997619378
- Cited by
- 27
- References
- 11
- Citations by year
- 2025: 3 · 2024: 2 · 2022: 1 · 2021: 3 · 2020: 3 · 2019: 3 · 2018: 4 · 2017: 2 · 2016: 3 · 2015: 2 · 2013: 1
- FWCI
- 0.68
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- raindrop detection, raindrop removal, saliency maps, windshields, image inpainting, false alarm reduction, color and texture features
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
