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]
Citations
Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 1×
“We compare our convolutional network against a nonconvolutional patch-level network similar to [2], as well as three baseline approaches: median filtering, bilateral filtering [18, 15], and BM3D [3].”
MethodSparse coding with an overcomplete basis set: A strategy employed by V1?
Olshausen, B. A., & Field, D. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00169-7 · Cited by 3,642
Image denoising using scale mixtures of gaussians in the wavelet domain
Portilla, J., Strela, V., Wainwright, M. J., & Simoncelli, E. P. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.818640 · Cited by 2,280
From learning models of natural image patches to whole image restoration
Zoran, D., & Weiss, Y. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126278 · Cited by 1,522
Image denoising: Can plain neural networks compete with BM3D?
Burger, H. C., Schuler, C. J., & Harmeling, S. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247952 · Cited by 1,325
Image Denoising and Inpainting with Deep Neural Networks
Xie, J., Xu, L., & Chen, E. (2012) · Cited by 1,237
(2008) · Cited by 838
A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach
Paris, S., & Durand, F. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0110-8 · Cited by 796
Image denoising with block-matching and 3D filtering
Dabov, K., Foi, A., Katkovnik, V. Y., & Egiazarian, K. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.643267 · Cited by 737
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Self-Learning Video Rain Streak Removal: When Cyclic Consistency Meets Temporal Correspondence
Yang, W., Tan, R. T., Wang, S., & Liu, J. (2020) · 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/cvpr42600.2020.00179 · Cited by 82
On Vectorization of Deep Convolutional Neural Networks for Vision Tasks
Ren, J., & Li, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9488 · Cited by 86
Single Image Rain Streak Decomposition Using Layer Priors
Li, Y., Tan, R. T., Guo, X., Lu, J., & Brown, M. S. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2708841 · Cited by 82
DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification
Zhang, S., He, R., Sun, Z., & Tan, T. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2763119 · Cited by 81
On-demand Learning for Deep Image Restoration
Gao, R., & Grauman, K. L. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.124 · Cited by 80
ERL-Net: Entangled Representation Learning for Single Image De-Raining
Wang, G., Sun, C., & Sowmya, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00574 · Cited by 80
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Zhang, K., Luo, W., Yu, Y., Ren, W., Zhao, F., Li, C., Ma, L., Liu, W., & Li, H. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01620-w · Cited by 80
Rain Streaks Removal for Single Image via Kernel-Guided Convolutional Neural Network
Wang, Y.-T., Zhao, X.-L., Jiang, T.-X., Deng, L.-J., Chang, Y., & Huang, T. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015897 · Cited by 79
RYF-Net: Deep Fusion Network for Single Image Haze Removal
Dudhane, A. A., & Murala, S. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2934360 · Cited by 82
Vision transformers for dense prediction: A survey
Zuo, S., Xiao, Y., Chang, X., & Wang, X. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109552 · Cited by 84
Chen, S., Tian, Y., Bai, J., Chen, E., Shi, J., & Zhu, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01205 · Cited by 77
Enhanced bi-prediction with convolutional neural network for high-efficiency video coding
Zhao, Z., Wang, S., Wang, S., Zhang, X., Ma, S., & Yang, J. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2876399 · Cited by 75
Deblurring Face Images Using Uncertainty Guided Multi-Stream Semantic Networks
Yasarla, R., Perazzi, F., & Patel, V. M. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2990354 · Cited by 74
Akilan, T., Wu, Q. M. J., & Zhang, W. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2937076 · Cited by 70
Li, S., Ren, W., Wang, F., Araujo, I. B., Tokuda, E. K., Hirata, R., Junior, R. M. C., Wang, Z., & Cao, X. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01416-w · Cited by 69
Single Image Deraining via Recurrent Hierarchy Enhancement Network
Yang, Y., & Lu, H. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3351149 · Cited by 67
Non-blind Deblurring: Handling Kernel Uncertainty with CNNs
Vasu, S., Maligireddy, V. R., & Rajagopalan, A. N. (2018) · 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.2018.00345 · Cited by 65
Structure-Preserving Image Super-Resolution via Contextualized Multitask Learning
Shi, Y., Wang, K., Chen, C., Xu, L., & Lin, L. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2711263 · Cited by 65
Multi-weather city: Adverse weather stacking for autonomous driving
Muşat, V., Fursa, I., Newman, P. M., Fabio Cuzzolin, A., & Bradley, A. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00325 · Cited by 63
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Huang, H., Yu, A., Chai, Z., He, R., & Tan, T. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01421-z · Cited by 60
CNN for license plate motion deblurring
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Hao, Z., You, S., Li, Y., Li, K., & Lu, F. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00534 · Cited by 54
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Marathe, A., Ramanan, D., Walambe, R. A., & Kotecha, K. V. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00334 · Cited by 51
A Neural Approach to Blind Motion DeblurringPreprint
Chakrabarti, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.04771 · Cited by 51
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Shao, M., Li, L., Meng, D., & Zuo, W. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3076283 · Cited by 50
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Brophy, T., Mullins, D., Parsi, A., Horgan, J., Ward, E., Denny, P., Eising, C., Deegan, B. M., Glavin, M., & Jones, E. (2023) · IEEE Access · doi:10.1109/access.2023.3290143 · Cited by 52
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Li, J., Feng, Y., Shao, Y., & Liu, F. (2024) · Applied Sciences · doi:10.3390/app14125277 · Cited by 48
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Tursun, O., Denman, S., Zeng, R., Sivapalan, S., Sridharan, S., & Fookes, C. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2020.103066 · Cited by 48
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Nah, S., Kim, T. H., & Lee, K. M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.02177 · Cited by 54
Real-time semantic segmentation for autonomous driving: A review of CNNs, Transformers, and Beyond
Elhassan, M. A. M., Zhou, C., Khan, A. H., Benabid, A., Adam, A. B. M., Mehmood, A., & Wambugu, N. M. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.102226 · Cited by 53
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Hsu, W., & Chang, W.-C. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2022.109294 · Cited by 45
Image rain removal and illumination enhancement done in one go
Wan, Y., Cheng, Y., Shao, M., & Gonzàlez, J. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109244 · Cited by 43
Fast non-blind deconvolution via regularized residual networks with long/short skip-connections
Son, H., & Lee, S. (2017) · IEEE International Conference on Computational Photography (ICCP) · doi:10.1109/iccphot.2017.7951480 · Cited by 43
Blind Visual Motif Removal From a Single Image
Hertz, A., Fogel, S., Hanocka, R., Giryes, R., & Cohen‐Or, D. (2019) · 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.2019.00702 · Cited by 43
Joint blur kernel estimation and CNN for blind image restoration
Huang, L., & Xia, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.12.083 · Cited by 43
Single image dehazing based on learning of haze layers
Xiao, J., Shen, M.-Y., Lei, J., Zhou, J., Klette, R., & Sui, H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.007 · Cited by 43
On failures of RGB cameras and their effects in autonomous driving applications
Secci, F., & Ceccarelli, A. (2020) · 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) · doi:10.1109/issre5003.2020.00011 · Cited by 42
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Yang, W., Tan, R. T., Feng, J., Liu, J., Guo, Z., & Yan, S. (2016) · arXiv (Cornell University) · Cited by 40
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Ahsan, M. M., Li, Y., Zhang, J., Ahad, M. T., & Gupta, K. D. (2021) · Technologies · doi:10.3390/technologies9020031 · Cited by 40
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Kim, D., & Hong, B. (2021) · IEEE Access · doi:10.1109/access.2021.3065701 · Cited by 35
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Machot, F. A., Ali, M., Mosa, A. H., Schwarzlmüller, C., Gutmann, M., & Kyamakya, K. (2016) · Journal of Real-Time Image Processing · doi:10.1007/s11554-016-0569-z · Cited by 34
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Xu, G., Wang, X., & Xu, X. (2020) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003423 · Cited by 33
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Shen, X., Chen, Y.-C., Tao, X., & Jia, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.02071 · Cited by 31
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Microscopy Image Restoration with Deep Wiener-Kolmogorov Filters
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Sun, H., Ang, M. H., & Rus, D. L. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967644 · Cited by 30
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Lightweight Semi-supervised Network for Single Image Rain Removal
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A total variation and group sparsity based tensor optimization model for video rain streak removal
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When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
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Weakly Supervised Learning for Raindrop Removal on a Single Image
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Lian, Q., Yan, W., Zhang, X., & Chen, S. (2019) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2019.1911441 · Cited by 23
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Estimation of Rate Control Parameters for Video Coding Using CNN
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A Review of Detection and Removal of Raindrops in Automotive Vision Systems
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“benchmarked their proposed algorithm against the ones proposed by Eigen [46], Qian [49], as well as Pix2Pix [52], and DIDMDN [60] algorithms that were not designed originally for adherent raindrop removal.”
BackgroundMethodInfluentialObject Proposal Generation With Fully Convolutional Networks
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Selective generative adversarial network for raindrop removal from a single image
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Single-image deraining via a Recurrent Memory Unit Network
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MCW-Net: Single image deraining with multi-level connections and wide regional non-local blocks
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Feature-Aligned Video Raindrop Removal With Temporal Constraints
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Fried, O., Shechtman, E., Goldman, D. B., & Finkelstein, A. (2015) · 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.2015.7298779 · Cited by 20
Deep Structured Prediction for Facial Landmark DetectionPreprint
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DerainGAN: Single image deraining using wasserstein GAN
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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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