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- W2982829842 abstract "In this paper, we propose a novel low-tubal-rank tensor recovery model, which directly constrains the tubal rank prior for effectively removing the mixed Gaussian and sparse noise in hyperspectral images. The constraints of tubal-rank and sparsity can govern the solution of the denoised tensor in the recovery procedure. To solve the constrained low-tubal-rank model, we develop an iterative algorithm based on bilateral random projections to efficiently solve the proposed model. The advantage of random projections is that the approximation of the low-tubal-rank tensor can be obtained quite accurately in an inexpensive manner. Experimental examples for hyperspectral image denoising are presented to demonstrate the effectiveness and efficiency of the proposed method." @default.
- W2982829842 created "2019-11-22" @default.
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- W2982829842 date "2019-07-01" @default.
- W2982829842 modified "2023-10-18" @default.
- W2982829842 title "Constrained Low-Tubal-Rank Tensor Recovery for Hyperspectral Images Mixed Noise Removal by Bilateral Random Projections" @default.
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- W2982829842 doi "https://doi.org/10.1109/igarss.2019.8899257" @default.
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