# Conditional Variational Image Deraining

arXiv: https://arxiv.org/abs/2004.11373 · HTML: https://mlsanity.com/paper/2004.11373/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 22 of 22)

- [Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models](https://mlsanity.com/paper/2207.14626/) (2022)
- [GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions](https://mlsanity.com/paper/2305.17863/) (2023)
- [Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond](https://mlsanity.com/paper/2203.16931/) (2022)
- [Generating Representative Samples for Few-Shot Classification](https://mlsanity.com/paper/2205.02918/) (2022)
- [Closing the Loop: Joint Rain Generation and Removal via Disentangled Image Translation](https://mlsanity.com/paper/2103.13660/) (2021)
- [Towards Unified Deep Image Deraining: A Survey and A New Benchmark](https://mlsanity.com/paper/2310.03535/) (2023)
- [Networks are Slacking Off: Understanding Generalization Problem in Image Deraining](https://mlsanity.com/paper/2305.15134/) (2023)
- [Unsupervised Image Deraining: Optimization Model Driven Deep CNN](https://mlsanity.com/paper/2203.13699/) (2022)
- [Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal](https://mlsanity.com/paper/2306.09008/) (2023)
- [Multi-scale Attentive Image De-raining Networks via Neural Architecture Search](https://mlsanity.com/paper/2207.00728/) (2022)
- [Uncertainty-Driven Action Quality Assessment](https://mlsanity.com/paper/2207.14513/) (2022)
- [A Hybrid Transformer-Mamba Network for Single Image Deraining](https://mlsanity.com/paper/2409.00410/) (2024)
- [Single Image Deraining Network with Rain Embedding Consistency and Layered LSTM](https://mlsanity.com/paper/2111.03615/) (2021)
- [Revisiting the Generalization Problem of Low-level Vision Models Through the Lens of Image Deraining](https://mlsanity.com/paper/2502.12600/) (2025)
- [SDNet: mutil-branch for single image deraining using swin](https://mlsanity.com/paper/2105.15077/) (2021)
- [Multiple weather images restoration using the task transformer and adaptive mixup strategy](https://mlsanity.com/paper/2409.03249/) (2024)
- [DA2Diff: Exploring Degradation-aware Adaptive Diffusion Priors for All-in-One Weather Restoration](https://mlsanity.com/paper/2504.05135/) (2025)
- [A General Divergence Modeling Strategy for Salient Object Detection](https://mlsanity.com/paper/2111.11827/) (2021)
- [Uncertainty-Aware Cascaded Dilation Filtering for High-Efficiency Deraining](https://mlsanity.com/paper/2201.02366/) (2022)
- [Rethinking Generative Methods for Image Restoration in Physics-based Vision: A Theoretical Analysis from the Perspective of Information](https://mlsanity.com/paper/2212.02198/) (2022)
- [MDeRainNet: An Efficient Macro-pixel Image Rain Removal Network](https://mlsanity.com/paper/2406.10652/) (2024)
- [Multi-Weather Image Restoration via Histogram-Based Transformer Feature Enhancement](https://mlsanity.com/paper/2409.06334/) (2024)

## Abstract (arXiv)

Image deraining is an important yet challenging image processing task. Though deterministic image deraining methods are developed with encouraging performance, they are infeasible to learn flexible representations for probabilistic inference and diverse predictions. Besides, rain intensity varies both in spatial locations and across color channels, making this task more difficult. In this paper, we propose a Conditional Variational Image Deraining (CVID) network for better deraining performance, leveraging the exclusive generative ability of Conditional Variational Auto-Encoder (CVAE) on providing diverse predictions for the rainy image. To perform spatially adaptive deraining, we propose a spatial density estimation (SDE) module to estimate a rain density map for each image. Since rain density varies across different color channels, we also propose a channel-wise (CW) deraining scheme. Experiments on synthesized and real-world datasets show that the proposed CVID network achieves much better performance than previous deterministic methods on image deraining. Extensive ablation studies validate the effectiveness of the proposed SDE module and CW scheme in our CVID network. The code is available at urlhttps://github.com/Yingjun-Du/VID.