PVRF: All-in-one Adverse Weather Removal via Prior-modulated and Velocity-constrained Rectified Flow
A unified framework integrating zero-shot soft weather perception and velocity-constrained rectified flow, using frozen vision-language models to guide restoration, achieving superior fidelity and perceptual quality with strong cross-dataset generalization.
[2605.14045] PVRF: All-in-one Adverse Weather Removal via Prior-modulated and Velocity-constrained Rectified Flow
[Submitted on 13 May 2026]
Title:PVRF: All-in-one Adverse Weather Removal via Prior-modulated and Velocity-constrained Rectified Flow
View a PDF of the paper titled PVRF: All-in-one Adverse Weather Removal via Prior-modulated and Velocity-constrained Rectified Flow, by Wei Dong and 8 other authors
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Abstract: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 this https URL.
Comments: 10 pages, 9 figures, and 4 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.14045 [cs.CV]
(or arXiv:2605.14045v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.14045
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Wei Dong [view email] [v1] Wed, 13 May 2026 19:07:14 UTC (28,587 KB)
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