# WeatherBench: A Real-World Benchmark Dataset for All-in-One Adverse Weather Image Restoration

arXiv: https://arxiv.org/abs/2509.11642 · HTML: https://mlsanity.com/paper/2509.11642/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 24 of 24)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [Benchmarking Single Image Dehazing and Beyond](https://mlsanity.com/paper/1712.04143/) (2017)
- [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)
- [Vision Transformers for Single Image Dehazing](https://mlsanity.com/paper/2204.03883/) (2022)
- [TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions](https://mlsanity.com/paper/2111.14813/) (2021)
- [Learning A Sparse Transformer Network for Effective Image Deraining](https://mlsanity.com/paper/2303.11950/) (2023)
- [DesnowNet: Context-Aware Deep Network for Snow Removal](https://mlsanity.com/paper/1708.04512/) (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)
- [Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images](https://mlsanity.com/paper/1904.02904/) (2019)
- [Single Image Deraining: A Comprehensive Benchmark Analysis](https://mlsanity.com/paper/1903.08558/) (2019)
- [Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model](https://mlsanity.com/paper/2403.11157/) (2024)
- [Restoring Images in Adverse Weather Conditions via Histogram Transformer](https://mlsanity.com/paper/2407.10172/) (2024)
- [Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining](https://mlsanity.com/paper/2404.01547/) (2024)
- [Toward Real-world Single Image Deraining: A New Benchmark and Beyond](https://mlsanity.com/paper/2206.05514/) (2022)
- [Towards Unified Deep Image Deraining: A Survey and A New Benchmark](https://mlsanity.com/paper/2310.03535/) (2023)
- [Depth Information Assisted Collaborative Mutual Promotion Network for Single Image Dehazing](https://mlsanity.com/paper/2403.01105/) (2024)
- [MWFormer: Multi-Weather Image Restoration Using Degradation-Aware Transformers](https://mlsanity.com/paper/2411.17226/) (2024)
- [Testing of Autonomous Driving Systems: Where Are We and Where Should We Go?](https://mlsanity.com/paper/2106.12233/) (2021)
- [SnowFormer: Context Interaction Transformer with Scale-awareness for Single Image Desnowing](https://mlsanity.com/paper/2208.09703/) (2022)
- [Surveilling Surveillance: Estimating the Prevalence of Surveillance Cameras with Street View Data](https://mlsanity.com/paper/2105.01764/) (2021)

## Built on by (most cited first, 20 of 20)

- [FoundIR-v2: Optimizing Pre-Training Data Mixtures for Image Restoration Foundation Model](https://mlsanity.com/paper/2512.09282/) (2025)
- [RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models](https://mlsanity.com/paper/2603.25502/) (2026)
- [Rethinking Nighttime Image Deraining via Learnable Color Space Transformation](https://mlsanity.com/paper/2510.17440/) (2025)
- [LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results](https://mlsanity.com/paper/2604.19445/) (2026)
- [DeLiVR: Differential Spatiotemporal Lie Bias for Efficient Video Deraining](https://mlsanity.com/paper/2509.21719/) (2025)
- [TPGDiff: Hierarchical Triple-Prior Guided Diffusion for Image Restoration](https://mlsanity.com/paper/2601.20306/) (2026)
- [V-Bridge: Bridging Video Generative Priors to Versatile Few-shot Image Restoration](https://mlsanity.com/paper/2603.13089/) (2026)
- [UniRain: Unified Image Deraining with RAG-based Dataset Distillation and Multi-objective Reweighted Optimization](https://mlsanity.com/paper/2603.03967/) (2026)
- [X-Restormer++: 1st Place Solution for the UG2+ CVPR 2026 All-Weather Restoration Challenge](https://mlsanity.com/paper/2605.13258/) (2026)
- [SpikeRestormer: Towards Energy-Efficient All-in-One Image Restoration via Unified Event Reasoning](https://mlsanity.com/paper/2608.02290/) (2026)
- [PixRestore: Unified Image Restoration via Pixel Diffusion Transformer](https://mlsanity.com/paper/2608.16793/) (2026)
- [UHDRes: Ultra-High-Definition Image Restoration via Dual-Domain Decoupled Spectral Modulation](https://mlsanity.com/paper/2511.05009/) (2025)
- [GGT-100K: Generative Ground Truth for Generalizable Real-World Image Restoration](https://mlsanity.com/paper/2605.31039/) (2026)
- [The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results](https://mlsanity.com/paper/2607.21118/) (2026)
- [M2IR: Proactive All-in-One Image Restoration via Mamba-style Modulation and Mixture-of-Experts](https://mlsanity.com/paper/2603.14816/) (2026)
- [Your Pre-trained Diffusion Model Secretly Knows Restoration](https://mlsanity.com/paper/2604.04924/) (2026)
- [DDTNet: Degradation Disentanglement and Transfer Network for Test-Time All-in-One De-weathering Adaptation](https://mlsanity.com/paper/2606.16298/) (2026)
- [What to Remove, What to Preserve: Dual-Ambiguity Rectification for All-in-One Image Restoration](https://mlsanity.com/paper/2607.28526/) (2026)
- [A paired synthetic construction-site image dataset for robust computer vision under adverse conditions](https://mlsanity.com/paper/2609.24075/) (2026)
- [The Domain Is a Residue: Adapting Self-Supervised Features, Not Generators](https://mlsanity.com/paper/2609.37330/) (2026)

## Abstract (arXiv)

Existing all-in-one image restoration approaches, which aim to handle multiple weather degradations within a single framework, are predominantly trained and evaluated using mixed single-weather synthetic datasets. However, these datasets often differ significantly in resolution, style, and domain characteristics, leading to substantial domain gaps that hinder the development and fair evaluation of unified models. Furthermore, the lack of a large-scale, real-world all-in-one weather restoration dataset remains a critical bottleneck in advancing this field. To address these limitations, we present a real-world all-in-one adverse weather image restoration benchmark dataset, which contains image pairs captured under various weather conditions, including rain, snow, and haze, as well as diverse outdoor scenes and illumination settings. The resulting dataset provides precisely aligned degraded and clean images, enabling supervised learning and rigorous evaluation. We conduct comprehensive experiments by benchmarking a variety of task-specific, task-general, and all-in-one restoration methods on our dataset. Our dataset offers a valuable foundation for advancing robust and practical all-in-one image restoration in real-world scenarios. The dataset has been publicly released and is available at https://github.com/guanqiyuan/WeatherBench.