Guided Feature Fusion: Zero-Shot Framework for Hyperspectral Image Denoising

Hyperspectral image (HSI) denoising algorithms often face challenges with non-uniform, band-dependent noise and fail to fully utilize high signal-to-noise ratio (SNR) spectral bands. To address these issues, this paper presents a zero-shot framework that leverages high-SNR bands as scene-specific references to guide the restoration of degraded bands. The framework features a dual-branch network architecture with the guide branch extracting high-fidelity spatial structures from high-SNR bands and the denoising branch restoring degraded bands through dynamic feature fusion. Fusion modules, enhanced with spectral-spatial attention, enable adaptive and spatially aware refinement between branches. Unlike conventional methods relying on external training datasets or fixed priors, the proposed approach adapts to the scene-specific quality disparities within each HSI. Extensive experiments on synthetic and real-world datasets demonstrate its superior ability in preserving spatial-spectral fidelity and handling complex noise scenarios, outperforming state-of-the-art methods.