Progressive Conditional Diffusion Model for Multistage Spectral Restoration of Remote Sensing Image

Due to the high cost and relatively low image quality of hyperspectral sensors, spectral super-resolution seeks to explore the mapping mechanisms between multispectral and hyperspectral images, with the goal of reconstructing high-quality hyperspectral images. In recent years, deep learning algorithms have achieved significant success in spectral super-resolution. However, most of these methods compute loss only at the final stage of the network, neglecting the intermediate generative processes, which leads to considerable spectral distortion in the reconstructed images. To address these issues, we propose a Progressive Conditional Diffusion Model (PCDM) for multi-stage spectral restoration. PCDM constructs a channel synthesis module that generates a ground truth set through band synthesis, and designs an image reconstruction module to ensure that the synthesized image in the next stage can effectively reconstruct the synthesized features from the previous stage. Multiple conditional diffusion models are then constructed based on the dataset. For each conditional diffusion model, the network parameters of the corresponding image reconstruction module are shared with the multispectral image for spectral up-sampling. The spectral-up-sampled multispectral features, combined with the output from the previous diffusion model, serve as a conditional matrix, which is input into the next diffusion model to obtain the final result. Experimental results on both synthetic and real datasets demonstrate that PCDM can effectively reconstruct hyperspectral images, showing robustness and outperforming state-of-the-art methods.