Learning to adapt unknown noise for hyperspectral image denoising (2301.06081v2)

Abstract: For hyperspectral image (HSI) denoising task, the causes of noise embeded in an HSI are typically complex and uncontrollable. Thus, it remains a challenge for model-based HSI denoising methods to handle complex noise. To enhance the noise-handling capabilities of existing model-based methods, we resort to design a general weighted data fidelity term. The weight in this term is used to assess the noise intensity and thus elementwisely adjust the contribution of the observed noisy HSI in a denoising model. The similar concept of "weighting" has been hinted in several methods. Due to the unknown nature of the noise distribution, the implementation of "weighting" in these works are usually achieved via empirical formula for specific denoising method. In this work, we propose to predict the weight by a hyper-weight network (i.e., HWnet). The HWnet is learned exactly from several model-based HSI denoising methods in a bi-level optimization framework based on the data-driven methodology. For a noisy HSI, the learned HWnet outputs its corresponding weight. Then the weighted data fidelity term implemented with the predicted weight can be explicitly combined with a target model-based HSI denoising method. In this way, our HWnet achieves the goal of enhancing the noise adaptation ability of model-based HSI denoising methods for different noisy HSIs. Extensive experiments verify that the proposed HWnet can effecitvely help to improve the ability of an HSI denoising model to handle different complex noises. This further implies that our HWnet could transfer the noise knowledge at the model level and we also study the corresponding generalization theory for simple illustration.