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Multi-way projections-based reconstruction for hyperspectral image denoising

In this paper, we propose a multi-way projections-based reconstruction method for noise reduction of hyperspectral image (HSI). Core ideas of the proposed method are twofold: 1) the original HSI is partitioned into many small three-dimensional (3D) patches. Each of the patch is taken as a third-order tensor, on which compressive multi-way measurements are performed; 2) denoised patches are produced by the approximate low multilinear-rank reconstructions, and the final denoised HSI can be obtained by putting the denoised patches back to where they are in the original HSI. Experiments conducted on the real hyperspectral data set demonstrate the promising performance of the proposed method.