Deep spatio-spectral Bayesian posterior for hyperspectral image non-i.i.d. noise removal

Abstract The noise pollution issue seriously obstructs subsequent interpretation and application of the hyperspectral image (HSI). In this work, differing from most existing HSI denoising methods ideally assumed that noise in different bands denotes independent & identically distributed (i.i.d.), we propose a novel HSI denoising approach focusing on non-i.i.d. noise removal. The presented framework collaboratively models the non-i.i.d. noise embedding within HSI and removals them under a deep spatio-spectral Bayesian posterior (DSSBP) structure. Specifically, the non-i.i.d. noise estimation, distribution and removal procedure are both executed with the model-driven based strategy and data-driven based strategy. Through blending the Bayesian variational posterior and deep convolutional neural network, the proposed method both inherits the reliability of traditional model-driven based methods for HSI noise modeling and the high efficiency of data-driven based methods for parameters learning. Simulated and real experiments in different HSIs and non-i.i.d. noise scenarios testify that the proposed DSSBP approach outperforms other existing methods for non-i.i.d. noise removal, in terms of evaluation indexes and executive efficiency.