SPGM-Net: Self-Supervised Network of Poisson-Gaussian Mixed Noise Removal for Real-World High-Dimensional Data

Poisson noise and Gaussian noise are widely present in high-dimensional data, which poses a challenging problem. Moreover, Poisson noise is signal-dependent and influenced by sensor parameters and the imaging environment; therefore, methods based on the additive Gaussian model are limited in handling such real-world scenarios. Meanwhile, self-supervised learning-based network frameworks can construct labels from noisy images for network training, addressing the issue of matching between noisy and clean images. However, while existing frameworks are very effective in handling Gaussian noise, the presence of a mixture of Poisson noise and Gaussian noise suppresses their performance. To address this issue, we propose a Poisson-Gaussian mixed noise removal framework, SPGM-Net, which is the first method for high-dimensional data Poisson-Gaussian mixture noise removal based on a self-supervised learning framework. First, we propose a self-supervised noise estimation network based on the generalized Anscombe transformation (GAT) noise transformation formula, which transforms noise into approximately Gaussian noise. Second, based on subspace representation theory, we build a series of training samples for self-supervised network training using a neighbor pixel sampler on the transformed noisy eigenimages. No matter in the noise estimation stage or the self-supervised denoising stage, only noisy images are used, which is suitable for real-world scenes. To verify the effectiveness of the proposed method, we test it on various data, including indoor hyperspectral images (HSIs) and real satellite images. The results of both the simulated data and the real data show that the proposed method is superior to state-of-the-art methods.