ISPDiff: Interpretable Scale-Propelled Diffusion Model for Hyperspectral Image Super-Resolution

Hyperspectral image (HSI) super-resolution (SR) employing the denoising diffusion probabilistic model (DDPM) holds significant promise with its remarkable performance. However, existing relevant works exhibit two limitations: i) Directly applying DDPM to fusion-based HSI SR (HSI-SR) ignores the physical mechanism of HSI-SR and unique characteristics of HSI, resulting in less interpretability; ii) Scale-invariant DDPM suffers from a time-consuming inference. To tackle these issues, we propose an interpretable scale-propelled diffusion model (ISPDiff) for HSI-SR, which combines the underlying principles of HSI-SR with DDPM for progressively unrolling reconstruction by learning its distribution at various scales, enhancing the transparency significantly and reducing the inference time prominently. Concretely, we destroy and downsample HSI into Gaussian noise in the forward process of ISPDiff. Then we design a unified scale-flexible model in the backward process to iteratively refine HSI in a coarse-to-fine manner through scale-matched reconstruction and cross-scale upsampling, which can be unfolded with optimization algorithms. These solved equations are one-to-one corresponding unrolled into two deep neural networks, called progressive perceptual model-driven scale-matched restoration network (P 2 MSRN) and cross-scale model-driven upsampling network (CMUN). Through end-to-end training, the proposed ISPDiff implements HSI-SR with a scale-propelled unrolling diffusion characterized by enhanced interpretability, stronger task orientation, and reduced time consumption. Systematic experiments have been conducted on three public datasets, demonstrating that ISPDiff outperforms state-of-the-art methods. Code is available at https://github.com/Jiahuiqu/ISPDiff.