# Rain O'er Me: Synthesizing real rain to derain with data distillation

arXiv: https://arxiv.org/abs/1904.04605 · HTML: https://mlsanity.com/paper/1904.04605/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [Multi-scale Attentive Image De-raining Networks via Neural Architecture Search](https://mlsanity.com/paper/2207.00728/) (2022)
- [Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation](https://mlsanity.com/paper/2409.02404/) (2024)
- [ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images](https://mlsanity.com/paper/2203.09275/) (2022)
- [MDeRainNet: An Efficient Macro-pixel Image Rain Removal Network](https://mlsanity.com/paper/2406.10652/) (2024)
- [End-to-end Inception-Unet based Generative Adversarial Networks for Snow and Rain Removals](https://mlsanity.com/paper/2411.04821/) (2024)
- [Adaptive 3D Convolution for Remote Sensing Image Fusion](https://mlsanity.com/paper/2605.09455/) (2026)

## Abstract (arXiv)

We present a supervised technique for learning to remove rain from images without using synthetic rain software. The method is based on a two-stage data distillation approach: 1) A rainy image is first paired with a coarsely derained version using on a simple filtering technique ("rain-to-clean"). 2) Then a clean image is randomly matched with the rainy soft-labeled pair. Through a shared deep neural network, the rain that is removed from the first image is then added to the clean image to generate a second pair ("clean-to-rain"). The neural network simultaneously learns to map both images such that high resolution structure in the clean images can inform the deraining of the rainy images. Demonstrations show that this approach can address those visual characteristics of rain not easily synthesized by software in the usual way.