  Hyperspectral image (HSI) denoising has been attracting much research
attention in remote sensing area due to its importance in improving the HSI
qualities. The existing HSI denoising methods mainly focus on specific spectral
and spatial prior knowledge in HSIs, and share a common underlying assumption
that the embedded noise in HSI is independent and identically distributed
(i.i.d.). In real scenarios, however, the noise existed in a natural HSI is
always with much more complicated non-i.i.d. statistical structures and the
under-estimation to this noise complexity often tends to evidently degenerate
the robustness of current methods. To alleviate this issue, this paper attempts
the first effort to model the HSI noise using a non-i.i.d. mixture of Gaussians
(NMoG) noise assumption, which is finely in accordance with the noise
characteristics possessed by a natural HSI and thus is capable of adapting
various noise shapes encountered in real applications. Then we integrate such
noise modeling strategy into the low-rank matrix factorization (LRMF) model and
propose a NMoG-LRMF model in the Bayesian framework. A variational Bayes
algorithm is designed to infer the posterior of the proposed model. All
involved parameters can be recursively updated in closed-form. Compared with
the current techniques, the proposed method performs more robust beyond the
state-of-the-arts, as substantiated by our experiments implemented on synthetic
and real noisy HSIs.
