PromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency Adaptation

TL;DR

PromptHSI introduces a universal hyperspectral image restoration framework that leverages frequency-aware modulation and vision-language prompts to effectively handle diverse degradations, outperforming previous methods in accuracy and versatility.

Contribution

It is the first to integrate frequency analysis and vision-language prompts for universal hyperspectral image restoration, addressing domain gaps and degradation complexities.

Findings

Outperforms existing methods in diverse degradation scenarios

Effectively captures HSI-specific degradation patterns

Achieves both fine-grained and global restoration

Abstract

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.