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In this paper, we propose an alternating directional 3D quasi-recurrentneural network for hyperspectral image (HSI) denoising, which can effectivelyembed the domain knowledge -- structural spatio-spectral correlation and globalcorrelation along spectrum. Specifically, 3D convolution is utilized to extractstructural spatio-spectral correlation in an HSI, while a quasi-recurrentpooling function is employed to capture the global correlation along spectrum.Moreover, alternating directional structure is introduced to eliminate thecausal dependency with no additional computation cost. The proposed model iscapable of modeling spatio-spectral dependency while preserving the flexibilitytowards HSIs with arbitrary number of bands. Extensive experiments on HSIdenoising demonstrate significant improvement over state-of-the-arts undervarious noise settings, in terms of both restoration accuracy and computationtime. Our code is available at https://github.com/Vandermode/QRNN3D.

3D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising