Block-Gaussian-Mixture Priors for Hyperspectral Denoising and Inpainting

摘要

This article proposes a denoiser for hyperspectral (HS) images that consider, not only spatial features, but also spectral features. The method starts by projecting the noisy (observed) HS data onto a lower dimensional subspace and then learns a Gaussian mixture model (GMM) from 3-D patches or blocks extracted from the projected data cube. Afterward, the minimum mean squared error (MMSE) estimates of the blocks are obtained in closed form and returned to their original positions. Experiments show that the proposed algorithm is able to outperform other state-of-the-art methods under Gaussian and Poissonian noise and to reconstruct high-quality images in the presence of stripes.