Constrained Low-Tubal-Rank Tensor Recovery for Hyperspectral Images Mixed Noise Removal by Bilateral Random Projections

Hao Zhang; Xi Le Zhao; Tai Xiang Jiang; Michael Kwok Po Ng

doi:10.1109/IGARSS.2019.8899257

Constrained Low-Tubal-Rank Tensor Recovery for Hyperspectral Images Mixed Noise Removal by Bilateral Random Projections

Hao Zhang
, Xi Le Zhao^*
, Tai Xiang Jiang
, Michael Kwok Po Ng

^*Corresponding author for this work

Department of Mathematics

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

4 Citations (Scopus)

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.

Original language	English
Title of host publication	2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
Publisher	IEEE
Pages	1939-1942
Number of pages	4
ISBN (Electronic)	9781538691540
DOIs	https://doi.org/10.1109/IGARSS.2019.8899257
Publication status	Published - Jul 2019
Event	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan Duration: 28 Jul 2019 → 2 Aug 2019 https://igarss2019.org/ https://igarss2019.org/TechnicalProgram.asp https://ieeexplore.ieee.org/xpl/conhome/8891871/proceeding

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/Territory	Japan
City	Yokohama
Period	28/07/19 → 2/08/19
Internet address	https://igarss2019.org/ https://igarss2019.org/TechnicalProgram.asp https://ieeexplore.ieee.org/xpl/conhome/8891871/proceeding

User-Defined Keywords

bilateral random projections
hyperspectral images
low-tubal-rank
mixed noise
Tensor

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10.1109/IGARSS.2019.8899257

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Zhang, H., Zhao, X. L., Jiang, T. X., & Ng, M. K. P. (2019). Constrained Low-Tubal-Rank Tensor Recovery for Hyperspectral Images Mixed Noise Removal by Bilateral Random Projections. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings (pp. 1939-1942). Article 8899257 (International Geoscience and Remote Sensing Symposium (IGARSS)). IEEE. https://doi.org/10.1109/IGARSS.2019.8899257

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title = "Constrained Low-Tubal-Rank Tensor Recovery for Hyperspectral Images Mixed Noise Removal by Bilateral Random Projections",

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.",

keywords = "bilateral random projections, hyperspectral images, low-tubal-rank, mixed noise, Tensor",

author = "Hao Zhang and Zhao, \{Xi Le\} and Jiang, \{Tai Xiang\} and Ng, \{Michael Kwok Po\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",

year = "2019",

month = jul,

doi = "10.1109/IGARSS.2019.8899257",

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Zhang, H, Zhao, XL, Jiang, TX & Ng, MKP 2019, Constrained Low-Tubal-Rank Tensor Recovery for Hyperspectral Images Mixed Noise Removal by Bilateral Random Projections. in 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings., 8899257, International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, pp. 1939-1942, 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japan, 28/07/19. https://doi.org/10.1109/IGARSS.2019.8899257

Constrained Low-Tubal-Rank Tensor Recovery for Hyperspectral Images Mixed Noise Removal by Bilateral Random Projections. / Zhang, Hao; Zhao, Xi Le; Jiang, Tai Xiang et al.
2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. IEEE, 2019. p. 1939-1942 8899257 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AU - Zhao, Xi Le

AU - Jiang, Tai Xiang

AU - Ng, Michael Kwok Po

PY - 2019/7

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N2 - 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.

AB - 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.

KW - bilateral random projections

KW - hyperspectral images

KW - low-tubal-rank

KW - mixed noise

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Zhang H, Zhao XL, Jiang TX, Ng MKP. Constrained Low-Tubal-Rank Tensor Recovery for Hyperspectral Images Mixed Noise Removal by Bilateral Random Projections. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. IEEE. 2019. p. 1939-1942. 8899257. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2019.8899257

Constrained Low-Tubal-Rank Tensor Recovery for Hyperspectral Images Mixed Noise Removal by Bilateral Random Projections

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