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

Hyperspectral image (HSI) denoising algorithms often face challenges with nonuniform, band-dependent noise and fail to fully utilize high signal-to-noise ratio (SNR) spectral bands. To address these issues, this article 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.