PromptHSI: Universal Hyperspectral Image Restoration Framework for Composite Degradation

Chia-Ming Lee, Ching-Heng Cheng, Yu-Fan Lin, Yi‐Ching Cheng, Wun-Rong Liao, Chih–Chung Hsu, Fu-En Yang, Yu-Chiang Frank Wang

Abstract

Recent developments in All-in-One (AiO) RGB image restoration and prompt learning have enabled the representation of distinct degradations through prompts, allowing degraded images to be effectively addressed by a single restoration model. However, this paradigm faces significant challenges when transferring to hyperspectral image (HSI) restoration tasks due to: 1) the domain gap between RGB and HSI features and difference on their structures, 2) information loss in visual prompts under severe composite degradations, and 3) difficulties in capturing HSI-specific degradation representations through text prompts. To address these challenges, we propose PromptHSI, the first universal AiO HSI restoration framework. By leveraging the frequency-aware feature modulation based on characteristics of HSI degradations, we decompose text prompts into intensity and bias controllers to effectively guide the restoration process while avoiding domain gaps. Our unified architecture excels at both fine-grained recovery and global information restoration tasks. Experimental results demonstrate superior performance under various degradation combinations, indicating great potential for practical remote sensing applications. The source code and dataset will be publicly released.

Locations

• arXiv (Cornell University) - View - PDF