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Spectral nonlocal restoration of hyperspectral images with low-rank property

Zhu, Rui, Dong, Mingzhi, Xue, Jinghao (2014) Spectral nonlocal restoration of hyperspectral images with low-rank property. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (6). pp. 3062-3067. ISSN 1939-1404. (doi:10.1109/JSTARS.2014.2370062) (KAR id:63937)

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Restoration is important in preprocessing hyperspectral images (HSI) to improve their visual quality and the accuracy in target detection or classification. In this paper, we propose a new low-rank spectral nonlocal approach (LRSNL) to the simultaneous removal of a mixture of different types of noises, such as Gaussian noises, salt and pepper impulse noises, and fixed-pattern noises including stripes and dead pixel lines. The low-rank (LR) property is exploited to obtain precleaned patches, which can then be better clustered in our spectral nonlocal method (SNL). The SNL method takes both spectral and spatial information into consideration to remove mixed noises as well as preserve the fine structures of images. Experiments on both synthetic and real data demonstrate that LRSNL, although simple, is an effective approach to the restoration of HSI.

Item Type: Article
DOI/Identification number: 10.1109/JSTARS.2014.2370062
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: R. Zhu
Date Deposited: 10 Oct 2017 19:58 UTC
Last Modified: 16 Feb 2021 13:49 UTC
Resource URI: (The current URI for this page, for reference purposes)
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