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Article: Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations

TitleFast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations
Authors
Keywordslow-dimensional subspace
BM4D
high-dimensional data
BM3D
self-similarity
nonlocal patch (cube)
low-rank regularized collaborative filtering
Issue Date2018
Citation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, v. 11, n. 3, p. 730-742 How to Cite?
AbstractThis paper introduces two very fast and competitive hyperspectral image (HSI) restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with Gaussian and Poissonian noise, and fast hyperspectral inpainting (FastHyIn), an inpainting algorithm to restore HSIs where some observations from known pixels in some known bands are missing. FastHyDe and FastHyIn fully exploit extremely compact and sparse HSI representations linked with their low-rank and self-similarity characteristics. In a series of experiments with simulated and real data, the newly introduced FastHyDe and FastHyIn compete with the state-of-the-art methods, with much lower computational complexity.
Persistent Identifierhttp://hdl.handle.net/10722/298251
ISSN
2023 Impact Factor: 4.7
2023 SCImago Journal Rankings: 1.434
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhuang, Lina-
dc.contributor.authorBioucas-Dias, José M.-
dc.date.accessioned2021-04-08T03:08:00Z-
dc.date.available2021-04-08T03:08:00Z-
dc.date.issued2018-
dc.identifier.citationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, v. 11, n. 3, p. 730-742-
dc.identifier.issn1939-1404-
dc.identifier.urihttp://hdl.handle.net/10722/298251-
dc.description.abstractThis paper introduces two very fast and competitive hyperspectral image (HSI) restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with Gaussian and Poissonian noise, and fast hyperspectral inpainting (FastHyIn), an inpainting algorithm to restore HSIs where some observations from known pixels in some known bands are missing. FastHyDe and FastHyIn fully exploit extremely compact and sparse HSI representations linked with their low-rank and self-similarity characteristics. In a series of experiments with simulated and real data, the newly introduced FastHyDe and FastHyIn compete with the state-of-the-art methods, with much lower computational complexity.-
dc.languageeng-
dc.relation.ispartofIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing-
dc.subjectlow-dimensional subspace-
dc.subjectBM4D-
dc.subjecthigh-dimensional data-
dc.subjectBM3D-
dc.subjectself-similarity-
dc.subjectnonlocal patch (cube)-
dc.subjectlow-rank regularized collaborative filtering-
dc.titleFast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/JSTARS.2018.2796570-
dc.identifier.scopuseid_2-s2.0-85041858658-
dc.identifier.volume11-
dc.identifier.issue3-
dc.identifier.spage730-
dc.identifier.epage742-
dc.identifier.eissn2151-1535-
dc.identifier.isiWOS:000427425000005-
dc.identifier.issnl1939-1404-

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