# EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining

arXiv: https://arxiv.org/abs/2009.09238 · HTML: https://mlsanity.com/paper/2009.09238/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2020.

## Built on by (most cited first, 30 of 39)

- [Auto-Exposure Fusion for Single-Image Shadow Removal](https://mlsanity.com/paper/2103.01255/) (2021)
- [Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather](https://mlsanity.com/paper/2210.15176/) (2022)
- [MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting](https://mlsanity.com/paper/2203.06304/) (2022)
- [Towards Unified Deep Image Deraining: A Survey and A New Benchmark](https://mlsanity.com/paper/2310.03535/) (2023)
- [Not Just Streaks: Towards Ground Truth for Single Image Deraining](https://mlsanity.com/paper/2206.10779/) (2022)
- [Leveraging Inpainting for Single-Image Shadow Removal](https://mlsanity.com/paper/2302.05361/) (2023)
- [Distilling Semantic Priors from SAM to Efficient Image Restoration Models](https://mlsanity.com/paper/2403.16368/) (2024)
- [JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting](https://mlsanity.com/paper/2107.04281/) (2021)
- [Learning A Spiking Neural Network for Efficient Image Deraining](https://mlsanity.com/paper/2405.06277/) (2024)
- [Let There be Light: Improved Traffic Surveillance via Detail Preserving Night-to-Day Transfer](https://mlsanity.com/paper/2105.05011/) (2021)
- [Background-Mixed Augmentation for Weakly Supervised Change Detection](https://mlsanity.com/paper/2211.11478/) (2022)
- [Computational Optics for Mobile Terminals in Mass Production](https://mlsanity.com/paper/2305.05886/) (2023)
- [SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Deraining](https://mlsanity.com/paper/2111.08892/) (2021)
- [SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers](https://mlsanity.com/paper/2112.09426/) (2021)
- [From heavy rain removal to detail restoration: A faster and better network](https://mlsanity.com/paper/2205.03553/) (2022)
- [Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather](https://mlsanity.com/paper/2307.09676/) (2023)
- [Transcoded Video Restoration by Temporal Spatial Auxiliary Network](https://mlsanity.com/paper/2112.07948/) (2021)
- [TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain](https://mlsanity.com/paper/2311.00660/) (2023)
- [AllRestorer: All-in-One Transformer for Image Restoration under Composite Degradations](https://mlsanity.com/paper/2411.10708/) (2024)
- [Learning Global-aware Kernel for Image Harmonization](https://mlsanity.com/paper/2305.11676/) (2023)
- [Learning Real-World Image De-Weathering with Imperfect Supervision](https://mlsanity.com/paper/2310.14958/) (2023)
- [Source-Free Object Detection with Detection Transformer](https://mlsanity.com/paper/2510.11090/) (2025)
- [Vision-Language Model Guided Image Restoration](https://mlsanity.com/paper/2512.17292/) (2025)
- [DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking](https://mlsanity.com/paper/2104.11585/) (2021)
- [SDNet: mutil-branch for single image deraining using swin](https://mlsanity.com/paper/2105.15077/) (2021)
- [Towards an Effective and Efficient Transformer for Rain-by-snow Weather Removal](https://mlsanity.com/paper/2304.02860/) (2023)
- [Exploring the Potentials of Spiking Neural Networks for Image Deraining](https://mlsanity.com/paper/2512.02258/) (2025)
- [Spiking Pyramid Wavelet Transformation for High-efficient and Low-energy Image Restoration](https://mlsanity.com/paper/2606.18644/) (2026)
- [Rain Removal and Illumination Enhancement Done in One Go](https://mlsanity.com/paper/2108.03873/) (2021)
- [Joint Conditional Diffusion Model for Image Restoration with Mixed Degradations](https://mlsanity.com/paper/2404.07770/) (2024)

## Abstract (arXiv)

Single-image deraining is rather challenging due to the unknown rain model. Existing methods often make specific assumptions of the rain model, which can hardly cover many diverse circumstances in the real world, making them have to employ complex optimization or progressive refinement. This, however, significantly affects these methods' efficiency and effectiveness for many efficiency-critical applications. To fill this gap, in this paper, we regard the single-image deraining as a general image-enhancing problem and originally propose a model-free deraining method, i.e., EfficientDeRain, which is able to process a rainy image within 10~ms (i.e., around 6~ms on average), over 80 times faster than the state-of-the-art method (i.e., RCDNet), while achieving similar de-rain effects. We first propose the novel pixel-wise dilation filtering. In particular, a rainy image is filtered with the pixel-wise kernels estimated from a kernel prediction network, by which suitable multi-scale kernels for each pixel can be efficiently predicted. Then, to eliminate the gap between synthetic and real data, we further propose an effective data augmentation method (i.e., RainMix) that helps to train network for real rainy image handling.We perform comprehensive evaluation on both synthetic and real-world rainy datasets to demonstrate the effectiveness and efficiency of our method. We release the model and code in https://github.com/tsingqguo/efficientderain.git.