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Volume: 32 | Article ID: art00018
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Hyperspectral complex-domain image denoising: cube complex-domain BM3D (CCDBM3D) algorithm
Vladimir Katkovnik
Mykola Ponomarenko
Karen Egiazarian
Igor Shevkunov
Peter Kocsis
  DOI :  10.2352/ISSN.2470-1173.2020.10.IPAS-179  Published Online :  January 2020
Abstract

We consider hyperspectral phase/amplitude imaging from hyperspectral complex-valued noisy observations. Block-matching and grouping of similar patches are main instruments of the proposed algorithms. The search neighborhood for similar patches spans both the spectral and 2D spatial dimensions. SVD analysis of 3D grouped patches is used for design of adaptive nonlocal bases. Simulation experiments demonstrate high efficiency of developed state-of-the-art algorithms.

Journal Title : Electronic Imaging
Publisher Name : Society for Imaging Science and Technology
Publisher Location : 7003 Kilworth Lane, Springfield, VA 22151 USA
Subject Areas :
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 DOI 10.2352/ISSN.2470-1173.2020.10.IPAS-179
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Vladimir Katkovnik, Mykola Ponomarenko, Karen Egiazarian, Igor Shevkunov, Peter Kocsis, "Hyperspectral complex-domain image denoising: cube complex-domain BM3D (CCDBM3D) algorithm"  in Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XVIII,  2020,  pp 179-1 - 179-7,  https://doi.org/10.2352/ISSN.2470-1173.2020.10.IPAS-179

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Copyright © Society for Imaging Science and Technology 2020
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Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA
10.2352/ISSN.2470-1173.2020.10.IPAS-179
2470-1173(20200126)2020:10L.1791;1-
ei_24701173_v2020n10_input/s19.xml
/ist/ei/2020/00002020/00000010/art00018
Articles
Hyperspectral complex-domain image denoising: cube complex-domain BM3D (CCDBM3D) algorithm
KatkovnikVladimir
PonomarenkoMykola
EgiazarianKaren
ShevkunovIgor
KocsisPeter
26012020
2020
10
179-1
179-7
2020
We consider hyperspectral phase/amplitude imaging from hyperspectral complex-valued noisy observations. Block-matching and grouping of similar patches are main instruments of the proposed algorithms. The search neighborhood for similar patches spans both the spectral and 2D spatial dimensions. SVD analysis of 3D grouped patches is used for design of adaptive nonlocal bases. Simulation experiments demonstrate high efficiency of developed state-of-the-art algorithms.
Block matchingComplex domainDenoisingHyperspectral imagingSparsity