# Rethinking Image Deraining via Rain Streaks and Vapors

arXiv: https://arxiv.org/abs/2008.00823 · HTML: https://mlsanity.com/paper/2008.00823/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 24 of 24)

- [VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples](https://mlsanity.com/paper/2103.05905/) (2021)
- [Parser-Free Virtual Try-on via Distilling Appearance Flows](https://mlsanity.com/paper/2103.04559/) (2021)
- [ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows](https://mlsanity.com/paper/2103.16877/) (2021)
- [PD-GAN: Probabilistic Diverse GAN for Image Inpainting](https://mlsanity.com/paper/2105.02201/) (2021)
- [Disentangled Cycle Consistency for Highly-realistic Virtual Try-On](https://mlsanity.com/paper/2103.09479/) (2021)
- [Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining](https://mlsanity.com/paper/2404.01547/) (2024)
- [Towards Unified Deep Image Deraining: A Survey and A New Benchmark](https://mlsanity.com/paper/2310.03535/) (2023)
- [Enhanced Spatio-Temporal Interaction Learning for Video Deraining: A Faster and Better Framework](https://mlsanity.com/paper/2103.12318/) (2021)
- [DeFLOCNet: Deep Image Editing via Flexible Low-level Controls](https://mlsanity.com/paper/2103.12723/) (2021)
- [Dual Attention-in-Attention Model for Joint Rain Streak and Raindrop Removal](https://mlsanity.com/paper/2103.07051/) (2021)
- [Networks are Slacking Off: Understanding Generalization Problem in Image Deraining](https://mlsanity.com/paper/2305.15134/) (2023)
- [Multi-scale Attentive Image De-raining Networks via Neural Architecture Search](https://mlsanity.com/paper/2207.00728/) (2022)
- [Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity](https://mlsanity.com/paper/2211.00837/) (2022)
- [Revisiting the Generalization Problem of Low-level Vision Models Through the Lens of Image Deraining](https://mlsanity.com/paper/2502.12600/) (2025)
- [Seeing Through the Rain: Resolving High-Frequency Conflicts in Deraining and Super-Resolution via Diffusion Guidance](https://mlsanity.com/paper/2511.12419/) (2025)
- [ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images](https://mlsanity.com/paper/2203.09275/) (2022)
- [Detail-recovery Image Deraining via Dual Sample-augmented Contrastive Learning](https://mlsanity.com/paper/2204.02772/) (2022)
- [Removing Rain Streaks via Task Transfer Learning](https://mlsanity.com/paper/2208.13133/) (2022)
- [Towards Better De-raining Generalization via Rainy Characteristics Memorization and Replay](https://mlsanity.com/paper/2506.02477/) (2025)
- [Exploiting Global and Local Attentions for Heavy Rain Removal on Single Images](https://mlsanity.com/paper/2104.08126/) (2021)
- [Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal](https://mlsanity.com/paper/2204.13420/) (2022)
- [End-to-end Inception-Unet based Generative Adversarial Networks for Snow and Rain Removals](https://mlsanity.com/paper/2411.04821/) (2024)
- [Unifying Physically-Informed Weather Priors in A Single Model for Image Restoration Across Multiple Adverse Weather Conditions](https://mlsanity.com/paper/2605.13158/) (2026)
- [DerainSplat: Feed-Forward Clean 3D Gaussian Splatting from Sparse Rainy Views](https://mlsanity.com/paper/2608.02191/) (2026)

## Abstract (arXiv)

Single image deraining regards an input image as a fusion of a background image, a transmission map, rain streaks, and atmosphere light. While advanced models are proposed for image restoration (i.e., background image generation), they regard rain streaks with the same properties as background rather than transmission medium. As vapors (i.e., rain streaks accumulation or fog-like rain) are conveyed in the transmission map to model the veiling effect, the fusion of rain streaks and vapors do not naturally reflect the rain image formation. In this work, we reformulate rain streaks as transmission medium together with vapors to model rain imaging. We propose an encoder-decoder CNN named as SNet to learn the transmission map of rain streaks. As rain streaks appear with various shapes and directions, we use ShuffleNet units within SNet to capture their anisotropic representations. As vapors are brought by rain streaks, we propose a VNet containing spatial pyramid pooling (SSP) to predict the transmission map of vapors in multi-scales based on that of rain streaks. Meanwhile, we use an encoder CNN named ANet to estimate atmosphere light. The SNet, VNet, and ANet are jointly trained to predict transmission maps and atmosphere light for rain image restoration. Extensive experiments on the benchmark datasets demonstrate the effectiveness of the proposed visual model to predict rain streaks and vapors. The proposed deraining method performs favorably against state-of-the-art deraining approaches.