用视觉语言模型感知天气，精准修复恶劣天气图像。
PVRF: All-in-one Adverse Weather Removal via Prior-modulated and Velocity-constrained Rectified Flow
- 通过冻结的视觉语言模型估算天气类型和低层属性概率
- 引入速度约束的修正流，提升恢复结果的真实感与稳定性
- 无需训练即可跨数据集通用，适合真实场景图像修复
由于真实世界图像中天气退化多样且未知，恶劣天气去除（AWR）仍具挑战性，而基于失真驱动的训练常导致结果过度平滑。本文提出PVRF，一种统一框架，融合零样本软天气感知与速度约束的修正流精修。PVRF引入专用于AWR的问题问答模块（AWR-QA），利用冻结的视觉-语言模型（VLMs）估计天气类型软概率及低层属性得分。这些感知通过属性调制归一化（AMN）与天气加权适配器（WWA）条件化恢复网络，生成初始估计供精修。随后，学习终端一致的残差修正流，采用感知自适应源扰动与终端一致的速度参数化，稳定终端区域的学习。大量实验表明，PVRF在保真度与感知质量上均优于现有最佳方法，在单个与组合退化下具备强跨数据集泛化能力。代码将发布于 https://github.com/dongw22/PVRF。
原文摘要 · Abstract (English)
Adverse weather removal (AWR) in real-world images remains challenging due to heterogeneous and unseen degradations, while distortion-driven training often yields overly smooth results. We propose PVRF, a unified framework that integrates zero-shot soft weather perceptions with velocity-constrained rectified-flow refinement. PVRF introduces an AWR-specific question answering module (AWR-QA) that uses frozen vision--language models (VLMs) to estimate soft probabilities of weather types and low-level attribute scores. These perceptions condition restoration networks via attribute-modulated normalization (AMN) and weather-weighted adapters (WWA), producing an anchor estimate for refinement. We then learn a terminal-consistent residual rectified flow with perception-adaptive source perturbation and a terminal-consistent velocity parameterization to stabilize learning near the terminal regime. Extensive experiments show that PVRF improves both fidelity and perceptual quality over state-of-the-art baselines, with strong cross-dataset generalization on single and combined degradations. Code will be released at https://github.com/dongw22/PVRF.
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