arXiv 2503.090132025-11-04cs.CV

提示以恢复，恢复以提示：用于通用恶劣天气去除的循环提示

Prompt to Restore, Restore to Prompt: Cyclic Prompting for Universal Adverse Weather Removal

School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）；School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）

Rongxin Liao, Feng Li, Yanyan Wei, Zenglin Shi, Le Zhang, Huihui Bai, Meng Wang

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AI总结：

本文提出CyclicPrompt循环提示方法，通过复合上下文提示和擦除粘贴机制构建“提示-恢复-提示”流水线，有效整合天气知识与恢复先验，显著提升了通用恶劣天气去除的性能与泛化能力。

摘要：

通用恶劣天气去除（UAWR）旨在统一框架内处理各种天气退化。近期方法受使用预训练视觉语言模型（如CLIP）的提示学习启发，利用退化感知提示促进无天气图像恢复，取得显著改进。本文提出CyclicPrompt，一种创新的循环提示方法，旨在增强UAWR的有效性、适应性和泛化能力。CyclicPrompt包含两个关键组件：1）复合上下文提示，将天气相关信息和上下文感知表示整合到网络中以指导恢复。该提示通过将可学习的输入条件向量与天气特定知识结合，区别于以往方法，从而提升在各种退化下的适应性。2）擦除粘贴机制，在初始引导恢复后，用受限恢复先验替换天气特定知识，向复合提示引入高质量无天气概念，进一步微调恢复过程。因此，我们形成循环的“提示-恢复-提示”流水线，巧妙利用天气特定知识、文本上下文和可靠纹理。在合成和真实数据集上的大量实验验证了CyclicPrompt的优越性能。代码可在https://github.com/RongxinL/CyclicPrompt获取。

英文摘要：

Universal adverse weather removal (UAWR) seeks to address various weather degradations within a unified framework. Recent methods are inspired by prompt learning using pre-trained vision-language models (e.g., CLIP), leveraging degradation-aware prompts to facilitate weather-free image restoration, yielding significant improvements. In this work, we propose CyclicPrompt, an innovative cyclic prompt approach designed to enhance the effectiveness, adaptability, and generalizability of UAWR. CyclicPrompt Comprises two key components: 1) a composite context prompt that integrates weather-related information and context-aware representations into the network to guide restoration. This prompt differs from previous methods by marrying learnable input-conditional vectors with weather-specific knowledge, thereby improving adaptability across various degradations. 2) The erase-and-paste mechanism, after the initial guided restoration, substitutes weather-specific knowledge with constrained restoration priors, inducing high-quality weather-free concepts into the composite prompt to further fine-tune the restoration process. Therefore, we can form a cyclic "Prompt-Restore-Prompt" pipeline that adeptly harnesses weather-specific knowledge, textual contexts, and reliable textures. Extensive experiments on synthetic and real-world datasets validate the superior performance of CyclicPrompt. The code is available at: https://github.com/RongxinL/CyclicPrompt.

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