Cheng, Ching-HengChing-HengChengLee, Chia-MingChia-MingLeeLiao, Wo-TingWo-TingLiaoLin, Yu-FanYu-FanLinCheng, Yi-ChingYi-ChingChengYang, Fu-EnFu-EnYangYU-CHIANG WANGHsu, Chih-ChungChih-ChungHsu2026-03-262026-03-262026-02-0301962892https://www.scopus.com/record/display.uri?eid=2-s2.0-105029529578&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/736814Recent 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, adapting these approaches to hyperspectral image (HSI) restoration poses significant challenges 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. To address these limitations, we introduce PromptHSI, the first universal AiO HSI restoration framework that is both prompt-guided and frequency-aware. By incorporating frequency-aware feature modulation, which uses frequency analysis to decouple complex composite degradations, and by 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 detail recovery and global information restoration across diverse degradation scenarios, highlighting its significant potential for practical remote sensing applications.falseAll-in-one (AiO) image restorationfrequency modulationhyperspectral image (HSI) restorationvision-language modeljournal article10.1109/tgrs.2026.36605062-s2.0-105029529578