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Research Article | Open Access

Multispectral image denoising using sparse and graph Laplacian Tucker decomposition

Xiaoce Wu1, Bingyin Zhou1( ), Qingyun Ren1, Wei Guo1
Key Laboratory of Augmented Reality, School of Mathematical Science, Hebei Normal University, Shijiazhuang 050024, China
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Abstract

Multispectral image denoising is a basic problem whose results affect subsequent processes such as target detection and classification. Numerous approaches have been proposed, but there are still many challenges, particularly in using prior knowledge of multispectral images, which is crucial for solving the ill-posed problem of noise removal. This paper considers both non-local self-similarity in space and global correlation in spectrum. We propose a novel low-rank Tucker decomposition model for removing the noise, in which sparse and graph Laplacian regularization terms are employed to encode this prior knowledge. It can jointly learn a sparse and low-rank representation while preserving the local geometrical structure between spectral bands, so as to better capture simultaneously the correlation in spatial and spectral directions. We adopt the alternating direction method of multipliers to solve the resulting problem. Experiments demonstrate that the proposed method outperforms the state-of-the-art, such as cube-based and tensor-based methods, both quantitatively and qualitatively.

Keywords

denoisingtensor decompositionsparsitygraph Laplacianmultispectral image

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Computational Visual Media
Volume 6 Issue 3,
September 2020
Pages 319-331
DOI: 10.1007/s41095-020-0176-6
Cite this article:
Wu X, Zhou B, Ren Q, et al. Multispectral image denoising using sparse and graph Laplacian Tucker decomposition. Computational Visual Media, 2020, 6(3): 319-331. https://doi.org/10.1007/s41095-020-0176-6

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Received: 29 March 2020
Accepted: 26 April 2020
Published: 20 July 2020
© The Author(s) 2020

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