Unsupervised Hyperspectral Image Denoising via Spectral Learning Preference of Neural Networks

Existing hyperspectral denoising networks typically rely on large amounts of high-quality paired noisy–clean images for training, which are often unavailable. Moreover, the noise distribution in real hyperspectral images (HSIs) is complex and variable, making it challenging for existing networks to handle noise distributions not present in the training dataset, resulting in poor generalization. To address these issues, this paper proposes an unsupervised Hyperspectral image Denoising approach exploiting the spectral learning preference of neural networks with an adaptive early stopping strategy (termed HyDePre). Inspired by the Deep Image Prior, which reveals that neural networks tend to capture natural image structures before fitting noise, we observe that deep neural networks exhibit a similar learning preference in the spectral domain. Specifically, as training progresses, the network first fits smooth spectral feature curves and only later adapts to Gaussian noise and complex impulse noise. This observation provides an opportunity to use an early stopping strategy, allowing the network to fit only the clean spectral signals and thus achieve denoising. Our method does not require clean images for training, but instead optimizes network parameters to automatically learn prior spectral information from a single noisy image, modeling the intrinsic structure of the input data to uncover its underlying patterns.However, finding the optimal stopping point is challenging without access to clean images as sources of prior information. To tackle this challenge, we introduce an adaptive early stopping strategy based on the average spectral maximum variation of the reconstructed image, effectively preventing overfitting. The experimental results demonstrate that HyDePre outperforms existing methods in terms of both visual quality and quantitative metrics.