A Noise-Model-Free Hyperspectral Image Denoising Method Based on Diffusion Model

Hyperspectral Image (HSI) denoising is a crucial preprocessing step to ensure the accuracy of the subsequent HSI analysis and interpretation. Neural network methods recently achieve state-of-the-art performance in HSI denoising. Nevertheless, these methods are typically trained on specific noise models, which could limit their performance as noise models in HSI may vary across different spectral bands. To mitigate this problem, we introduce an HSI denoising method based on the Diffusion Model (DM) whose training process is independent of the noise model, noted as a noise-model-free method. In this method, we first introduce a DM for HSI by increasing the input and output dimensions to incorporate spectral and spatial information. We then derive a DM-based HSI denoising process for a common noise model. Moreover, to address the issue of overfitting during training, we have introduced a stochastic sampling method to more effectively balance the significance of both spatial and spectral information. Experimental results on in-distribution and out-of-distribution samples demonstrate the efficacy of our approach.