首个统一修复高光谱图像的通用框架，用视觉语言模型提升修复精度。
PromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency Adaptation
- 通过频域分析缩小修复搜索空间，结合视觉语言模型生成可控提示。
- 在多种退化场景下均实现精细恢复与全局信息重建，性能优于现有方法。
- 适合遥感图像修复、多模态学习等领域的研究人员快速应用。
近期基于提示学习的全功能RGB图像修复方法在单一模型中处理多种退化方面表现出色。然而，将此类方法扩展至高光谱图像（HSI）修复面临挑战：一是RGB与HSI特征间的领域差异，二是严重复合退化下视觉提示的信息损失，三是文本提示难以捕捉HSI特有的退化模式。本文提出PromptHSI，首个统一的全功能HSI修复框架。通过引入频域感知特征调制，利用频率分析缩小修复搜索空间，并采用视觉-语言模型（VLM）引导的提示学习，将文本提示分解为强度与偏置控制器，有效指导修复过程并缓解领域差异。大量实验表明，该统一架构在多样退化场景下均能实现精细恢复与全局信息重建，展现出显著的实用潜力。代码已开源：https://github.com/chingheng0808/PromptHSI。
原文摘要 · Abstract (English)
Recent advances in All-in-One (AiO) RGB image restoration have demonstrated the effectiveness of prompt learning in handling multiple degradations within a single model. However, extending these approaches to hyperspectral image (HSI) restoration is challenging due to the domain gap between RGB and HSI features, information loss in visual prompts under severe composite degradations, and difficulties in capturing HSI-specific degradation patterns via text prompts. In this paper, we propose PromptHSI, the first universal AiO HSI restoration framework that addresses these challenges. By incorporating frequency-aware feature modulation, which utilizes frequency analysis to narrow down the restoration search space and employing vision-language model (VLM)-guided prompt learning, our approach decomposes text prompts into intensity and bias controllers that effectively guide the restoration process while mitigating domain discrepancies. Extensive experiments demonstrate that our unified architecture excels at both fine-grained recovery and global information restoration across diverse degradation scenarios, highlighting its significant potential for practical remote sensing applications. The source code is available at https://github.com/chingheng0808/PromptHSI.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。