统一修复雨痕与雨滴，适配昼夜多种真实场景。
UniRain: Unified Image Deraining with RAG-based Dataset Distillation and Multi-objective Reweighted Optimization
- 用RAG技术精选多数据集优质样本，提升训练质量。
- 在多个公开数据集上优于现有模型，昼夜场景表现稳定。
- 适合需要通用化雨水去除的工业应用与研究者。
尽管图像去雨研究已取得显著进展，但多数方法仅针对特定类型的雨渍退化，难以泛化到多样化的现实雨天场景。如何在统一框架中有效建模不同雨渍退化，对真实场景去雨至关重要。本文提出UniRain，一种能同时恢复雨痕和雨滴退化、适用于昼夜条件的统一图像去雨框架。为增强模型泛化能力，我们构建了基于检索增强生成（RAG）的数据集提炼流程，从所有公开去雨数据集中筛选高质量训练样本以实现更优混合训练。此外，我们在非对称专家混合（MoE）架构中引入简单有效的多目标重加权优化策略，提升模型在多样化场景下的性能一致性与鲁棒性。大量实验表明，该框架在所提出的基准及多个公开数据集上均优于当前最优模型。
原文摘要 · Abstract (English)
Despite significant progress has been made in image deraining, we note that most existing methods are often developed for only specific types of rain degradation and fail to generalize across diverse real-world rainy scenes. How to effectively model different rain degradations within a universal framework is important for real-world image deraining. In this paper, we propose UniRain, an effective unified image deraining framework capable of restoring images degraded by rain streak and raindrop under both daytime and nighttime conditions. To better enhance unified model generalization, we construct an intelligent retrieval augmented generation (RAG)-based dataset distillation pipeline that selects high-quality training samples from all public deraining datasets for better mixed training. Furthermore, we incorporate a simple yet effective multi-objective reweighted optimization strategy into the asymmetric mixture-of-experts (MoE) architecture to facilitate consistent performance and improve robustness across diverse scenes. Extensive experiments show that our framework performs favorably against the state-of-the-art models on our proposed benchmarks and multiple public datasets.
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