# When Color-Space Decoupling Meets Diffusion for Adverse-Weather Image Restoration

arXiv: https://arxiv.org/abs/2509.17024 · HTML: https://mlsanity.com/paper/2509.17024/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 54 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking](https://mlsanity.com/paper/2412.15691/) (2024)
- [A-BDD: Leveraging Data Augmentations for Safe Autonomous Driving in Adverse Weather and Lighting](https://mlsanity.com/paper/2408.06071/) (2024)
- [AsyncDSB: Schedule-Asynchronous Diffusion Schr"odinger Bridge for Image Inpainting](https://mlsanity.com/paper/2412.08149/) (2024)

## Built on by (most cited first, 3 of 3)

- [Personalized federated prototype learning in mixed heterogeneous data scenarios](https://mlsanity.com/paper/2510.03726/) (2025)
- [Cross-Layer Feature Self-Attention Module for Multi-Scale Object Detection](https://mlsanity.com/paper/2510.14726/) (2025)
- [YCDa: YCbCr Decoupled Attention for Real-time Realistic Camouflaged Object Detection](https://mlsanity.com/paper/2603.01602/) (2026)

## Abstract (arXiv)

Adverse Weather Image Restoration (AWIR) is a highly challenging task due to the unpredictable and dynamic nature of weather-related degradations. Traditional task-specific methods often fail to generalize to unseen or complex degradation types, while recent prompt-learning approaches depend heavily on the degradation estimation capabilities of vision-language models, resulting in inconsistent restorations. In this paper, we propose LCDiff, a novel framework comprising two key components: textitLumina-Chroma Decomposition Network (LCDN) and textitLumina-Guided Diffusion Model (LGDM). LCDN processes degraded images in the YCbCr color space, separately handling degradation-related luminance and degradation-invariant chrominance components. This decomposition effectively mitigates weather-induced degradation while preserving color fidelity. To further enhance restoration quality, LGDM leverages degradation-related luminance information as a guiding condition, eliminating the need for explicit degradation prompts. Additionally, LGDM incorporates a textitDynamic Time Step Loss to optimize the denoising network, ensuring a balanced recovery of both low- and high-frequency features in the image. Finally, we present DriveWeather, a comprehensive all-weather driving dataset designed to enable robust evaluation. Extensive experiments demonstrate that our approach surpasses state-of-the-art methods, setting a new benchmark in AWIR. The dataset and code are available at: https://github.com/fiwy0527/LCDiff.