# Toward Real-world Single Image Deraining: A New Benchmark and Beyond

arXiv: https://arxiv.org/abs/2206.05514 · HTML: https://mlsanity.com/paper/2206.05514/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 48 references that are in the map.
- First posted on arXiv 11 Jun 2022.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [Deep Joint Rain Detection and Removal from a Single Image](https://mlsanity.com/paper/1609.07769/) (2016)
- [Learning Texture Transformer Network for Image Super-Resolution](https://mlsanity.com/paper/2006.04139/) (2020)
- [Progressive Image Deraining Networks: A Better and Simpler Baseline](https://mlsanity.com/paper/1901.09221/) (2019)
- [Multi-Scale Progressive Fusion Network for Single Image Deraining](https://mlsanity.com/paper/2003.10985/) (2020)
- [Density-aware Single Image De-raining using a Multi-stream Dense Network](https://mlsanity.com/paper/1802.07412/) (2018)
- [HINet: Half Instance Normalization Network for Image Restoration](https://mlsanity.com/paper/2105.06086/) (2021)
- [Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining](https://mlsanity.com/paper/1807.05698/) (2018)
- [Image De-raining Using a Conditional Generative Adversarial Network](https://mlsanity.com/paper/1701.05957/) (2017)
- [Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset](https://mlsanity.com/paper/1904.01538/) (2019)
- [Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning](https://mlsanity.com/paper/1904.05050/) (2019)
- [Clearing the Skies: A deep network architecture for single-image rain removal](https://mlsanity.com/paper/1609.02087/) (2016)
- [A Model-driven Deep Neural Network for Single Image Rain Removal](https://mlsanity.com/paper/2005.01333/) (2020)
- [Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining](https://mlsanity.com/paper/1906.11129/) (2019)
- [Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things](https://mlsanity.com/paper/2011.08612/) (2020)
- [Colorization Transformer](https://mlsanity.com/paper/2102.04432/) (2021)
- [Delving Deep into the Generalization of Vision Transformers under Distribution Shifts](https://mlsanity.com/paper/2106.07617/) (2021)
- [Lightweight Pyramid Networks for Image Deraining](https://mlsanity.com/paper/1805.06173/) (2018)
- [Structure-Preserving Deraining with Residue Channel Prior Guidance](https://mlsanity.com/paper/2108.09079/) (2021)
- [TAPE: Task-Agnostic Prior Embedding for Image Restoration](https://mlsanity.com/paper/2203.06074/) (2022)
- [HIPA: Hierarchical Patch Transformer for Single Image Super Resolution](https://mlsanity.com/paper/2203.10247/) (2022)
- [U2-Former: A Nested U-shaped Transformer for Image Restoration](https://mlsanity.com/paper/2112.02279/) (2021)

## Built on by (most cited first, 30 of 40)

- [Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration](https://mlsanity.com/paper/2408.15994/) (2024)
- [From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal](https://mlsanity.com/paper/2308.03867/) (2023)
- [FoundIR: Unleashing Million-scale Training Data to Advance Foundation Models for Image Restoration](https://mlsanity.com/paper/2412.01427/) (2024)
- [Towards Unified Deep Image Deraining: A Survey and A New Benchmark](https://mlsanity.com/paper/2310.03535/) (2023)
- [Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving](https://mlsanity.com/paper/2311.11056/) (2023)
- [Multi-Agent Image Restoration](https://mlsanity.com/paper/2503.09403/) (2025)
- [WeatherBench: A Real-World Benchmark Dataset for All-in-One Adverse Weather Image Restoration](https://mlsanity.com/paper/2509.11642/) (2025)
- [UAV-Rain1k: A Benchmark for Raindrop Removal from UAV Aerial Imagery](https://mlsanity.com/paper/2402.05773/) (2024)
- [Residual Diffusion Bridge Model for Image Restoration](https://mlsanity.com/paper/2510.23116/) (2025)
- [All-weather Multi-Modality Image Fusion: Unified Framework and 100k Benchmark](https://mlsanity.com/paper/2402.02090/) (2024)
- [GenDeg: Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration](https://mlsanity.com/paper/2411.17687/) (2024)
- [Channel Consistency Prior and Self-Reconstruction Strategy Based Unsupervised Image Deraining](https://mlsanity.com/paper/2503.18703/) (2025)
- [IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models](https://mlsanity.com/paper/2505.24406/) (2025)
- [Dual Degradation Representation for Joint Deraining and Low-Light Enhancement in the Dark](https://mlsanity.com/paper/2305.03997/) (2023)
- [Re-boosting Self-Collaboration Parallel Prompt GAN for Unsupervised Image Restoration](https://mlsanity.com/paper/2408.09241/) (2024)
- [DGSolver: Diffusion Generalist Solver with Universal Posterior Sampling for Image Restoration](https://mlsanity.com/paper/2504.21487/) (2025)
- [Rethinking Nighttime Image Deraining via Learnable Color Space Transformation](https://mlsanity.com/paper/2510.17440/) (2025)
- [UniRain: Unified Image Deraining with RAG-based Dataset Distillation and Multi-objective Reweighted Optimization](https://mlsanity.com/paper/2603.03967/) (2026)
- [Unpaired Image Deraining Using Reward-Guided Self-Reinforcement Strategy](https://mlsanity.com/paper/2605.00719/) (2026)
- [Learning Adaptive Dynamical Features via Multi-τ Liquid-Mamba for All-in-one Image Restoration](https://mlsanity.com/paper/2606.22801/) (2026)
- [PixRestore: Unified Image Restoration via Pixel Diffusion Transformer](https://mlsanity.com/paper/2608.16793/) (2026)
- [Invert2Restore: Zero-Shot Degradation-Blind Image Restoration](https://mlsanity.com/paper/2503.21486/) (2025)
- [From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration](https://mlsanity.com/paper/2505.22284/) (2025)
- [TAP: Parameter-efficient Task-Aware Prompting for Adverse Weather Removal](https://mlsanity.com/paper/2508.07878/) (2025)
- [SD-PSFNet: Sequential and Dynamic Point Spread Function Network for Image Deraining](https://mlsanity.com/paper/2511.17993/) (2025)
- [Language-Free Generative Editing from One Visual Example](https://mlsanity.com/paper/2603.25441/) (2026)
- [UENR-600K: A Large-Scale Physically Grounded Dataset for Nighttime Video Deraining](https://mlsanity.com/paper/2604.04402/) (2026)
- [DRNet: All-in-One Image Restoration via Prior-Guided Dynamic Reparameterization](https://mlsanity.com/paper/2605.08627/) (2026)
- [GGT-100K: Generative Ground Truth for Generalizable Real-World Image Restoration](https://mlsanity.com/paper/2605.31039/) (2026)
- [Causal-AgentIR: Self-Evolving Causal Memory for Adaptive Image Restoration Agents](https://mlsanity.com/paper/2607.21125/) (2026)

## Abstract (arXiv)

Single image deraining (SID) in real scenarios attracts increasing attention in recent years. Due to the difficulty in obtaining real-world rainy/clean image pairs, previous real datasets suffer from low-resolution images, homogeneous rain streaks, limited background variation, and even misalignment of image pairs, resulting in incomprehensive evaluation of SID methods. To address these issues, we establish a new high-quality dataset named RealRain-1k, consisting of 1,120 high-resolution paired clean and rainy images with low- and high-density rain streaks, respectively. Images in RealRain-1k are automatically generated from a large number of real-world rainy video clips through a simple yet effective rain density-controllable filtering method, and have good properties of high image resolution, background diversity, rain streaks variety, and strict spatial alignment. RealRain-1k also provides abundant rain streak layers as a byproduct, enabling us to build a large-scale synthetic dataset named SynRain-13k by pasting the rain streak layers on abundant natural images. Based on them and existing datasets, we benchmark more than 10 representative SID methods on three tracks: (1) fully supervised learning on RealRain-1k, (2) domain generalization to real datasets, and (3) syn-to-real transfer learning. The experimental results (1) show the difference of representative methods in image restoration performance and model complexity, (2) validate the significance of the proposed datasets for model generalization, and (3) provide useful insights on the superiority of learning from diverse domains and shed lights on the future research on real-world SID. The datasets will be released at https://github.com/hiker-lw/RealRain-1k