WeatherCycle: Unpaired Multi-Weather Restoration via Color Space Decoupled Cycle Learning

• URL: http://arxiv.org/abs/2509.23150v1
• Date: Sat, 27 Sep 2025 06:44:27 GMT
• Title: WeatherCycle: Unpaired Multi-Weather Restoration via Color Space Decoupled Cycle Learning
• Authors: Wenxuan Fang, Jiangwei Weng, Jianjun Qian, Jian Yang, Jun Li,
• Abstract summary: Unsupervised image restoration under multi-weather conditions remains a fundamental yet underexplored challenge.<n>We propose textbfWeatherCycle, a unified framework that reformulates weather restoration as a bidirectional degradation-content translation cycle.<n>Our method achieves state-of-the-art performance among unsupervised approaches, with strong generalization to complex weather degradations.
• Score: 30.62082910458533
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unsupervised image restoration under multi-weather conditions remains a fundamental yet underexplored challenge. While existing methods often rely on task-specific physical priors, their narrow focus limits scalability and generalization to diverse real-world weather scenarios. In this work, we propose \textbf{WeatherCycle}, a unified unpaired framework that reformulates weather restoration as a bidirectional degradation-content translation cycle, guided by degradation-aware curriculum regularization. At its core, WeatherCycle employs a \textit{lumina-chroma decomposition} strategy to decouple degradation from content without modeling complex weather, enabling domain conversion between degraded and clean images. To model diverse and complex degradations, we propose a \textit{Lumina Degradation Guidance Module} (LDGM), which learns luminance degradation priors from a degraded image pool and injects them into clean images via frequency-domain amplitude modulation, enabling controllable and realistic degradation modeling. Additionally, we incorporate a \textit{Difficulty-Aware Contrastive Regularization (DACR)} module that identifies hard samples via a CLIP-based classifier and enforces contrastive alignment between hard samples and restored features to enhance semantic consistency and robustness. Extensive experiments across serve multi-weather datasets, demonstrate that our method achieves state-of-the-art performance among unsupervised approaches, with strong generalization to complex weather degradations.

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