# I Can See Clearly Now : Image Restoration via De-Raining

arXiv: https://arxiv.org/abs/1901.00893 · HTML: https://mlsanity.com/paper/1901.00893/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2019.

## Built on by (most cited first, 23 of 23)

- [Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges](https://mlsanity.com/paper/2205.13281/) (2022)
- [NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results](https://mlsanity.com/paper/2504.12711/) (2025)
- [Blind Image Decomposition](https://mlsanity.com/paper/2108.11364/) (2021)
- [Raindrop Clarity: A Dual-Focused Dataset for Day and Night Raindrop Removal](https://mlsanity.com/paper/2407.16957/) (2024)
- [NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results](https://mlsanity.com/paper/2604.10634/) (2026)
- [Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions](https://mlsanity.com/paper/2103.02760/) (2021)
- [Physics-informed Guided Disentanglement in Generative Networks](https://mlsanity.com/paper/2107.14229/) (2021)
- [RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration via Explainable AI Systems](https://mlsanity.com/paper/2204.01719/) (2022)
- [RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes](https://mlsanity.com/paper/2104.04606/) (2021)
- [Feature-Aligned Video Raindrop Removal with Temporal Constraints](https://mlsanity.com/paper/2205.14574/) (2022)
- [Video Waterdrop Removal via Spatio-Temporal Fusion in Driving Scenes](https://mlsanity.com/paper/2302.05916/) (2023)
- [StyleLess layer: Improving robustness for real-world driving](https://mlsanity.com/paper/2103.13905/) (2021)
- [UnfairGAN: An Enhanced Generative Adversarial Network for Raindrop Removal from A Single Image](https://mlsanity.com/paper/2110.05523/) (2021)
- [InstantIR: Blind Image Restoration with Instant Generative Reference](https://mlsanity.com/paper/2410.06551/) (2024)
- [Ensemble-based Semi-supervised Learning to Improve Noisy Soiling Annotations in Autonomous Driving](https://mlsanity.com/paper/2105.07930/) (2021)
- [Strong and Controllable Blind Image Decomposition](https://mlsanity.com/paper/2403.10520/) (2024)
- [Fast Image-Anomaly Mitigation for Autonomous Mobile Robots](https://mlsanity.com/paper/2109.01889/) (2021)
- [EBSnoR: Event-Based Snow Removal by Optimal Dwell Time Thresholding](https://mlsanity.com/paper/2208.10581/) (2022)
- [Unified Removal of Raindrops and Reflections: A New Benchmark and A Novel Pipeline](https://mlsanity.com/paper/2603.16446/) (2026)
- [Generating Clear Images From Images With Distortions Caused by Adverse Weather Using Generative Adversarial Networks](https://mlsanity.com/paper/2211.05234/) (2022)
- [Building Resilience to Out-of-Distribution Visual Data via Input Optimization and Model Finetuning](https://mlsanity.com/paper/2211.16228/) (2022)
- [Soiling detection for Advanced Driver Assistance Systems](https://mlsanity.com/paper/2511.09740/) (2025)
- [CURE: Controllable Unified Image Restoration for Complex Degradations](https://mlsanity.com/paper/2607.03044/) (2026)

## Abstract (arXiv)

We present a method for improving segmentation tasks on images affected by adherent rain drops and streaks. We introduce a novel stereo dataset recorded using a system that allows one lens to be affected by real water droplets while keeping the other lens clear. We train a denoising generator using this dataset and show that it is effective at removing the effect of real water droplets, in the context of image reconstruction and road marking segmentation. To further test our de-noising approach, we describe a method of adding computer-generated adherent water droplets and streaks to any images, and use this technique as a proxy to demonstrate the effectiveness of our model in the context of general semantic segmentation. We benchmark our results using the CamVid road marking segmentation dataset, Cityscapes semantic segmentation datasets and our own real-rain dataset, and show significant improvement on all tasks.