Degradation-Aware Metric Prompting for Hyperspectral Image Restoration

2025-12-23

AI Summary

Real-world hyperspectral image (HSI) degradation is complex, mixed, and difficult to annotate, yet existing methods rely on explicit degradation priors, limiting generalization. To address this, we propose a unified, degradation-label-free HSI restoration framework. First, we design a spatial-spectral joint degradation-aware metric to automatically generate degradation prompts. Second, we introduce a learnable Spatial-Spectral Adaptive Module (SSAM) and a degradation-prompt-driven Mixture-of-Experts (MoE) architecture, enabling shared representation learning and dynamic feature modulation across diverse degradation tasks. Our method achieves state-of-the-art performance on both natural and remote sensing HSI benchmarks, demonstrating strong generalization to unseen and mixed degradations. The source code is publicly available.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Computer Vision: Multi-modal Vision
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Unified hyperspectral image (HSI) restoration aims to recover various degraded HSIs using a single model, offering great practical value. However, existing methods often depend on explicit degradation priors (e.g., degradation labels) as prompts to guide restoration, which are difficult to obtain due to complex and mixed degradations in real-world scenarios. To address this challenge, we propose a Degradation-Aware Metric Prompting (DAMP) framework. Instead of relying on predefined degradation priors, we design spatial-spectral degradation metrics to continuously quantify multi-dimensional degradations, serving as Degradation Prompts (DP). These DP enable the model to capture cross-task similarities in degradation distributions and enhance shared feature learning. Furthermore, we introduce a Spatial-Spectral Adaptive Module (SSAM) that dynamically modulates spatial and spectral feature extraction through learnable parameters. By integrating SSAM as experts within a Mixture-of-Experts architecture, and using DP as the gating router, the framework enables adaptive, efficient, and robust restoration under diverse, mixed, or unseen degradations. Extensive experiments on natural and remote sensing HSI datasets show that DAMP achieves state-of-the-art performance and demonstrates exceptional generalization capability. Code is publicly available at https://github.com/MiliLab/DAMP.

Problem

• Develop a degradation-aware prompting framework for unified hyperspectral image restoration
• Quantify degradations without explicit prior knowledge using spatial-spectral metrics
• Enable adaptive restoration under mixed or unseen degradation conditions

Innovation

• Uses degradation metrics as prompts for restoration
• Introduces adaptive module for spatial-spectral feature modulation
• Integrates mixture-of-experts with degradation-aware routing