Degradation-Aware Metric Prompting for Hyperspectral Image Restoration
Binfeng Wang, Di Wang, Haonan Guo, Ying Fu, Jing Zhang

TL;DR
The paper introduces DAMP, a novel hyperspectral image restoration framework that uses degradation metrics as prompts to adaptively and robustly restore images without relying on explicit degradation labels.
Contribution
It proposes a degradation-aware prompting framework with spatial-spectral metrics and a mixture-of-experts architecture for improved hyperspectral image restoration.
Findings
Achieves state-of-the-art performance on HSI datasets.
Demonstrates strong generalization to unseen degradations.
Effectively handles diverse and mixed degradations.
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…
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Taxonomy
TopicsRemote-Sensing Image Classification · Advanced Image Fusion Techniques · Image and Signal Denoising Methods
