Mixed Poisson-Gaussian noise model based sparse denoising for hyperspectral imagery

Sparse representation has been applied to image denoising in recent years. It is based on the assumption that the non-noise component in the signal can be approximated by only a small number of atoms in a dictionary while the noise component cannot. Previous researches have shown its excellent ability of noise reduction for images with signal-independent Gaussian noise. However, hyperspectral imagery has both of signal-independent and signal-dependent noise, so a mixed Poisson-Gaussian noise model is always used. In order to make sparse denoising method deal with such more complex noise model rather than just Gaussian noise model, the variance-stabilizing transformation (VST) and its inverse transformation are used before and after sparse denoising. The parameter estimation method for the mixed Poisson-Gaussian noise model is also discussed in this paper.