Video-based raindrop detection for improved image registration
Open accessAbstract
In this paper we present a novel approach to improved image registration in rainy weather situations. To this end, we perform monocular raindrop detection in single images based on a photometric raindrop model. Our method is capable of detecting raindrops precisely, even in front of complex backgrounds. The effectiveness is demonstrated by a significant increase in image registration accuracy which also allows for successful image restoration. Experiments on video sequences taken from within a moving vehicle prove the applicability to real-world scenarios.
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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 [21]
In image restoration, fastRIGSEC performed well but fell slightly behind BLUR + RIGSEC, especially with an increase in raindrop density.
“In terms of image restoration, fastRIGSEC performed well but fall slightly behind (BLUR + RIGSEC), especially with an increase in raindrop density.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [21]
FastRIGSEC performed better than SURF and BLUR but fell behind BLUR + RIGSEC in terms of maximum precision achievable.
“FastRIGSEC performed better than SURF and BLUR but fall behind (BLUR + RIGSEC) in terms of maximum precision achievable.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [21]
FastRIGSEC performed better than the rest in terms of maximum recall rate achievable.
“FastRIGSEC still performed better than the rest in terms of maximum recall rate achievable.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [21]
Roser and Geiger created an enhanced version of the RIGSEC model named fastRIGSEC.
“Roser and Geiger [21] created an enhanced version of the RIGSEC model named fastRIGSEC.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [21]
The introduction of a blurry disk improved the resemblance between modeled raindrops and raindrop projections captured by the camera.
“The introduction of a blurry disk improved the resemblance between modeled raindrop and raindrop projection captured by the camera.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [21]
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Attentive Generative Adversarial Network for Raindrop Removal from A Single Image
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“For rain recovery, Roser and Geiger developed a reconstruction process, that can be summarized as follows: 1- Define Θi, a 6D vector that represents rotational and translational parameters between frames i and i + 1: Θi = ( rx(i), ry(i), rz(i), tx(i), ty(i), tz(i) ) The task is to solve for this vector and use it for…”
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- Authors
- Roser, M., & Geiger, A.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- ORCID
- Geiger, Andreas
- Venue
- 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 570-577
- ISSN
- 2473-9936 (ISSN-L)
- OpenAlex
- W2038133417
- Cited by
- 126
- References
- 21
- Citations by year
- 2025: 6 · 2024: 2 · 2023: 7 · 2022: 6 · 2021: 20 · 2020: 21 · 2019: 17 · 2018: 10 · 2017: 5 · 2016: 4 · 2015: 7 · 2014: 7 · 2013: 4 · 2012: 5
- FWCI
- 2.72
- Type
- conference paper
- Language
- en
- Open access
- green
- doi.org
- Licence
- other-oa
- Locations
- IEEE International Conference on Computer Vision Workshops (publishedVersion)
- Repository KITopen (Karlsruhe Institute of Technology) (publishedVersion)
- KITopen (other-oa)
- Funders
- Deutsche Forschungsgemeinschaft
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Stabilization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- raindrop detection, image registration, rainy weather, monocular vision, image restoration, video sequences, moving vehicles
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