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Spectral non-local restoration of hyperspectral images with low-rank property

Zhu, R. ORCID: 0000-0002-9944-0369, Dong, M. & Xue, J-H. (2014). Spectral non-local 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. doi: 10.1109/JSTARS.2014.2370062


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.

Publication Type: Article
Additional Information: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see
Publisher Keywords: Hyperspectral image, low rank (LR), nonlocal means, restoration, spectral and spatial information.
Subjects: H Social Sciences > HA Statistics
Q Science > QC Physics
Departments: Bayes Business School > Actuarial Science & Insurance
Text - Published Version
Available under License Creative Commons: Attribution 3.0.

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