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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The other three methods caused much degradation to non-dirt areas of the images.
“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]
Testing with snow and scratches resulted in near-zero PSNR, demonstrating that the system learned to remove dirt noise only.
“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]
Synthesized rain results showed that the proposed system outperformed the MLP system in removing more rain droplets and maintaining non-rain areas intact.
“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]
Synthetic dirt results showed that the proposed system and the MLP system outperformed the other three baseline methods.
“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]
Real-life dirt results showed that the proposed system removed most dirt noise while the MLP left many more unremoved.
“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]
The proposed system failed to recognize large dirt noises, out-of-shape dirt, and dirt over areas not represented in the training set.
“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]
The median filter needed to blur the image substantially before it was able to reduce raindrops.
“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]
Eigen et al. proposed a system to remove dirt and raindrop noise from a single image using the NN architecture originally developed by LeCun et al.
“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]
Real rain results showed that the proposed system successfully removed real raindrops but started failing with accumulation of water on the glass plate.
“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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RefineNet: Multi-Path Refinement Networks for Dense Prediction
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Rain Streak Removal via Dual Graph Convolutional Network
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Motion Blur Kernel Estimation via Deep Learning
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Deep Learning for Seeing Through Window With Raindrops
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Spatially-Adaptive Image Restoration using Distortion-Guided Networks
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Restoring Images in Adverse Weather Conditions via Histogram Transformer
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Deep Structured Learning for Facial Action Unit Intensity Estimation
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PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN
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Compression Artifacts Removal Using Convolutional Neural NetworksPreprint
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Rain Rendering for Evaluating and Improving Robustness to Bad Weather
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A Unified Approach of Multi-scale Deep and Hand-Crafted Features for Defocus Estimation
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Physics-Based Rendering for Improving Robustness to Rain
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Exploring Context with Deep Structured Models for Semantic Segmentation
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Rain Removal in Traffic Surveillance: Does it Matter?
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Scale-Free Single Image Deraining Via Visibility-Enhanced Recurrent Wavelet Learning
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Frame-Consistent Recurrent Video Deraining With Dual-Level Flow
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Dual Attention-in-Attention Model for Joint Rain Streak and Raindrop Removal
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End-to-end video background subtraction with 3d convolutional neural networks
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DSANet: Dilated spatial attention for real-time semantic segmentation in urban street scenes
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D3R-Net: Dynamic Routing Residue Recurrent Network for Video Rain Removal
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I Can See Clearly Now: Image Restoration via De-Raining
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Video Superresolution via Motion Compensation and Deep Residual Learning
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Spatial and Angular Resolution Enhancement of Light Fields Using Convolutional Neural Networks
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- 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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