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Article: Hyperspectral Mixed Noise Removal By ℓ1-Norm-Based Subspace Representation

TitleHyperspectral Mixed Noise Removal By ℓ<font size=-1><sub>1</sub></font>-Norm-Based Subspace Representation
Authors
KeywordsGaussian noise
Hyperspectral imaging
Transforms
Noise reduction
Additives
Issue Date2020
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443
Citation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, v. 13, p. 1143-1157 How to Cite?
AbstractThis article introduces a new hyperspectral image (HSI) denoising method that is able to cope with additive mixed noise, i.e., mixture of Gaussian noise, impulse noise, and stripes, which usually corrupt hyperspectral images in the acquisition process. The proposed method fully exploits a compact and sparse HSI representation based on its low-rank and self-similarity characteristics. In order to deal with mixed noise having a complex statistical distribution, we propose to use the robust ℓ 1 data fidelity instead of using the ℓ 1 data fidelity, which is commonly employed for Gaussian noise removal. In a series of experiments with simulated and real datasets, the proposed method competes with state-of-the-art methods, yielding better results for mixed noise removal.
Persistent Identifierhttp://hdl.handle.net/10722/288098
ISSN
2019 Impact Factor: 3.827
2015 SCImago Journal Rankings: 1.196
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhuang, L-
dc.contributor.authorNg, MK-
dc.date.accessioned2020-10-05T12:07:50Z-
dc.date.available2020-10-05T12:07:50Z-
dc.date.issued2020-
dc.identifier.citationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, v. 13, p. 1143-1157-
dc.identifier.issn1939-1404-
dc.identifier.urihttp://hdl.handle.net/10722/288098-
dc.description.abstractThis article introduces a new hyperspectral image (HSI) denoising method that is able to cope with additive mixed noise, i.e., mixture of Gaussian noise, impulse noise, and stripes, which usually corrupt hyperspectral images in the acquisition process. The proposed method fully exploits a compact and sparse HSI representation based on its low-rank and self-similarity characteristics. In order to deal with mixed noise having a complex statistical distribution, we propose to use the robust ℓ 1 data fidelity instead of using the ℓ 1 data fidelity, which is commonly employed for Gaussian noise removal. In a series of experiments with simulated and real datasets, the proposed method competes with state-of-the-art methods, yielding better results for mixed noise removal.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443-
dc.relation.ispartofIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectGaussian noise-
dc.subjectHyperspectral imaging-
dc.subjectTransforms-
dc.subjectNoise reduction-
dc.subjectAdditives-
dc.titleHyperspectral Mixed Noise Removal By ℓ<font size=-1><sub>1</sub></font>-Norm-Based Subspace Representation-
dc.typeArticle-
dc.identifier.emailNg, MK: michael.ng@hku.hk-
dc.identifier.authorityNg, MK=rp02578-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/JSTARS.2020.2979801-
dc.identifier.scopuseid_2-s2.0-85083452049-
dc.identifier.hkuros315736-
dc.identifier.volume13-
dc.identifier.spage1143-
dc.identifier.epage1157-
dc.identifier.isiWOS:000527687800001-
dc.publisher.placeUnited States-
dc.identifier.issnl1939-1404-

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