Uncertainty-Driven Generative Prior Learning for Sparse Model-Guided Hyperspectral Image Fusion

s an alternative to acquiring high-resolution hyperspectral images (HR-HSI), Hyperspectral Image Fusion (HIF) aims to recover clean HR-HSIs by fusing degraded low spatial resolution hyperspectral images and high spatial resolution multispectral images. Existing model-guided HIF methods elegantly integrate physical degradation constraints with the powerful learning capabilities of data-driven networks.s an alternative to acquiring high-resolution hyperspectral images (HR-HSI), Hyperspectral Image Fusion (HIF) aims to recover clean HR-HSIs by fusing degraded low spatial resolution hyperspectral images and high spatial resolution multispectral images. Existing model-guided HIF methods elegantly integrate physical degradation constraints with the powerful learning capabilities of data-driven networks.A However, these methods struggle with severe, unseen degradations, as their deep priors are trained on degraded-clean pairs and thus lack knowledge of clean HSI characteristics. To address these issues, we introduce a Vector-Quantized Prior-Guided Network (VPG-Net), which incorporates a novel uncertainty-driven generative model prior alongside a traditional sparse representation prior. Specifically, VPG-Net unfolds the Maximum A Posteriori (MAP) estimation with a sparse representation model into an uncertainty-aware generative prior-guided network implementation. The sparse representation prior is integrated into the MAP framework to improve noise resistance, whose solution is implemented as a trainable neural network. As the core of our contribution, we leverage a high-quality vector-quantized (VQ) prior, which serves as a powerful degradation-free generative prior for the HIF process. Specifically, we pre-train a discrete codebook and encoder from clean HR-HSIs to generate a VQ-prior representation (VQPR), which preserves complete spatial-spectral information. To effectively bridge the gap between degraded inputs and the learned degradation-free codebook, we incorporate a novel uncertainty-driven probabilistic matching strategy that robustly aligns features and prevents artifacts. The learned VQPR is then incorporated into the reconstruction model as dynamic modulation parameters to enhance the fidelity and realism of the reconstructed results, particularly for severely degraded inputs. Extensive experiments on clean and degraded synthetic and real-world datasets demonstrate that our approach outperforms state-of- the-art HIF methods in both quantitative metrics and visual quality.