COMBINED THE DATA-DRIVEN WITH MODEL-DRIVEN STRAGEGY: A NOVEL FRAMEWORK FOR MIXED NOISE REMOVAL IN HYPERSPECTRAL IMAGE

In this paper, we present a novel hyperspectral image (HSI) denoising method especially for mixed noise removal. The proposed method combines both data-driven with model-driven strategy via a deep spatiospectral variational structure. The mixed noise estimation and removal are collaboratively derived through fusing the Bayesian spatio-spectral posterior and deep learning model. The framework can both utilize the logicality of traditional model-driven methods, and the high efficiency of data-driven methods for parameters optimizing. Simulated and actual experiments demonstrate that the presented method outperforms other existing methods for HSI mixed noise removal, on both reconstructing effects and time-consuming.