An Adaptive Nonlocal Gaussian Prior for Hyperspectral Image Denoising

Nonlocal similar patches are effectively used in the Gaussian prior denoising model. However, it is difficult to learn an accurate Gaussian model for hyperspectral image (HSI) with noisy and limited similar patches, which will result in unstable Gaussian parameters (mean and covariance). In this letter, several techniques are proposed to overcome the noisy and small sample problems for HSI denoising. For Gaussian parameters, we propose the adaptive weighted mean of nonlocal similar patches and use a positive semidefinite constraint on the covariance parameter. In addition, an iterative manner is used to achieve more accurate parameters. The proposed method can achieve more robust Gaussian model for HSI denoising. Experiments on a HSI demonstrate the effectiveness of the proposed algorithm compared with the traditional methods for HSI denoising.