Blind Hyperspectral Image Denoising with Degradation Information Learning

Although existing hyperspectral image (HSI) denoising methods have exhibited promising performance in synthetic noise removal, they are seriously restricted in real-world scenarios with complicated noises. The major reason is that model-based methods largely rely on the noise type assumption and parameter setting, and learning-based methods perform poorly in generalizability due to the scarcity of real-world clean-noisy data pairs. To overcome this long-standing challenge, we propose a novel denoising method with degradation information learning (termed DIBD), which attempts to approximate the joint distribution of the clean-noisy HSI pairs in a Bayesian framework. Specifically, our framework learns the mappings of noisy-to-clean and clean-to-noisy in a priority dual regression scheme. We develop more comprehensive auxiliary information to simplify the joint distribution approximation process instead of only estimating noise intensity. Our method can leverage both labeled synthetic and unlabeled real data for learning. Extensive experiments show that the proposed DIBD achieves state-of-the-art performance on synthetic datasets and has better generalization to real-world HSIs. The source code will be available to the public.