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Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections
[article]
2019
arXiv
pre-print
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
arXiv:1905.05941v1
fatcat:oxq2p5237jfprac4ygbqzaldna