CFMDM: Coarse-to-Fine Meta-Diffusion Model for Scale-Arbitrary Hyperspectral Super-Resolution

Hyperspectral image super-resolution (HSISR) has shown very promising potential for earth observation and deep space exploration tasks. However, most existing HSISR methods formulate HSISR tasks with different scale factors as independent tasks and train a specific model for each scale factor. In this letter, we propose a coarse-to-fine meta-diffusion HSISR method, termed CFMDM, which is capable of solving the problem of HSISR with scale-arbitrary factors in a unified model. The proposed CFMDM is composed of a coarse-to-fine upsampling module. The module encompasses two pivotal units: a coarse meta-upsampling unit that utilizes meta-learning to map features of arbitrary scales to the corresponding scales and a gradual refinement diffusion unit that is designed to refine the details of the reconstructed HSI. In addition, we develop an imaging model-driven downsampling algorithm for generating training samples tailored to practical applications. The proposed method performs well in both quantitative and qualitative evaluation on benchmark datasets, achieving the average PSNR of 41.45 dB at 1.5x super-resolution for the CAVE dataset.