Hyperspectral imagery denoising using covariance matrix estimation based structured sparse coding and intra-cluster filtering

Sparse coding provides an excellent image prior for hyperspectral images (HSIs) denoising. However, on one hand, it is challenging to capture the structure within each sparse code for improving the reconstruction accuracy, on the other hand, the inconsistent recovery of the sparse codes corrupts the spectrum similarity in each homogeneous cluster of the HSI. To address these problems, we first propose a novel covariance matrix estimation based structured sparse coding method, where the sparse code matrix is modeled by a matrix normal distribution with a full covariance matrix. By estimating the covariance matrix with a latent variable based Bayesian framework, the data-dependent and noise-robust structure for each sparse code is learned from the noisy observation, with which the sparse codes are reconstructed accurately. Then, an intra-cluster filtering is employed to restore the spectrum similarity in each cluster. Experimental results demonstrate that the proposed method outperforms several state-of-the-art methods in HSIs denoising.