The DOI matches its Crossref record; 26 of the 27 references it cites were found in a registry or an index.
Restoring an Image Taken through a Window Covered with Dirt or Rain
Abstract
Photographs taken through a window are often compromised by dirt or rain present on the window surface. Common cases of this include pictures taken from inside a vehicle, or outdoor security cameras mounted inside a protective enclosure. At capture time, defocus can be used to remove the artifacts, but this relies on achieving a shallow depth-of-field and placement of the camera close to the window. Instead, we present a post-capture image processing solution that can remove localized rain and dirt artifacts from a single image. We collect a dataset of clean/corrupted image pairs which are then used to train a specialized form of convolutional neural network. This learns how to map corrupted image patches to clean ones, implicitly capturing the characteristic appearance of dirt and water droplets in natural images. Our models demonstrate effective removal of dirt and rain in outdoor test conditions.
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In the citing paper
“The other three methods caused much degradation to non-dirt areas of the images.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [46]
In the citing paper
“Testing with other types of sensitized noise, namely snow, and scratches on the image surface resulted in the system described by Eigen et al. producing near-zero PSNR (peak signal to noise ratio) which demonstrates that the system learned to remove dirt noise only.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [46]
In the citing paper
“Synthesized rain that the proposed system outperformed the MLP system, in terms of removing more rain droplets and maintain non-rain areas of the image intact.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [46]
In the citing paper
“Synthetic dirt results showed that the proposed system and the MLP system outperformed the other three baseline methods.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [46]
In the citing paper
“Real-life dirt results showed that the proposed system removed most dirt noise in the images, while the MLP left many more unremoved.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [46]
In the citing paper
“The proposed system failed to recognize large dirt noises and the ones that are out of shape, as well as dirt over areas that were not represented in the training set (e.g., bright orange cones).”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [46]
In the citing paper
“The median filter needed to blur the image substantially before it was able to reduce raindrops.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [46]
In the citing paper
“Eigen et al. [46] proposed a system to remove dirt and raindrop noise from a single image, using the NN architecture originally developed by LeCun et al. [47].”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [46]
In the citing paper
“Real rain results showed that the proposed system successfully removed real raindrops from the captured images, but it started failing with the accumulation of water on the glass plate.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [46]
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HDRD-Net: High-resolution detail-recovering image deraining network
Zhu, D., Deng, S., Wang, W., Cheng, G., Wei, M., Wang, F. L., & Xie, H. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13489-5 · Cited by 6
Wang, B., Liu, G., & Wu, J.-F. (2019) · Symmetry · doi:10.3390/sym11050678 · Cited by 6
MSL-MNN: image deraining based on multi-scale lightweight memristive neural network
Zhang, L., Zhou, Y., Hu, X., Sun, F., & Duan, S. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06835-5 · Cited by 6
Technology Gaps in First Responder Cameras
Pinson, M. (2017) · doi:10.70220/l83fl1hk · Cited by 6
A Deep Tree-Structured Fusion Model for Single Image DerainingPreprint
Fu, X., Qi, Q., Huang, Y., Ding, X., Wu, F., & Paisley, J. W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08632 · Cited by 6
Detection of Raindrop with Various Shapes on aWindshield
Ishizuka, J., & Onoguchi, K. (2016) · Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0005796004750483 · Cited by 5
“Eigen et al. (Eigen et al., 2013) removed small dirt or raindrops from a corrupt image by predicting a clean output by convolutional neural network.”
BackgroundExploiting Regional Information Transformer for Single Image Deraining
Li, B., Zhang, Z., Zheng, H., Xu, X., Wei, Y., Zhang, J., Fan, J., & Wang, M. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3651066 · Cited by 5
Dehazing using Generative Adversarial Network - A Review
Khatun, A., Mostafiz, R., Shorif, S. B., Uddin, M. S., & Hadi, M. A. (2024) · SN Computer Science · doi:10.1007/s42979-024-03571-0 · Cited by 5
Rethinking mixture of rain removal via depth-guided adversarial learning
Wang, Y., Yan, X., Niu, Y., Gong, L., Guo, Y., & Wei, M. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107739 · Cited by 5
Single Image Raindrop Removal Using a Non-Local Operator and Feature Maps in the Frequency Domain
Ezumi, S., & Ikehara, M. (2022) · IEEE Access · doi:10.1109/access.2022.3202888 · Cited by 5
Metadata
- Authors
- Eigen, D., Krishnan, D., & Fergus, R.
- Affiliations
- New York University (US)
- Venue
- 2013 IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 633-640
- OpenAlex
- W2154815154
- Semantic Scholar
- 2814db29a34e4ddc83f63c1c0d7b2ae467d337c6
- Cited by
- 454
- References
- 27
- Citations by year
- 2026: 6 · 2025: 24 · 2024: 27 · 2023: 34 · 2022: 44 · 2021: 63 · 2020: 62 · 2019: 48 · 2018: 48 · 2017: 42 · 2016: 35 · 2015: 17 · 2014: 4
- FWCI
- 11.91
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Funders
- National Science Foundation
- Microsoft Research
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- rain removal, dirt removal, image restoration, single image restoration, convolutional neural networks, outdoor images, water droplets
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