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

Hyperspectral complex-domain image denoising: cube complex-domain BM3D (CCDBM3D) algorithm

Vladimir Katkovnik, Mykola Ponomarenko, Karen Egiazarian, Igor Shevkunov, Peter Kocsis

Computing Sciences

Research output: Contribution to journal › Conference article › Scientific › peer-review

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.

Original language	English
Journal	IS and T International Symposium on Electronic Imaging Science and Technology
Volume	2020
Issue number	10
DOIs	https://doi.org/10.2352/ISSN.2470-1173.2020.10.IPAS-179
Publication status	Published - 26 Jan 2020
Publication type	A1 Journal article-refereed
Event	Image Processing: Algorithms and Systems Conference - Duration: 26 Jan 2020 → 30 Jan 2020

Keywords

Block matching
Complex domain
Denoising
Hyperspectral imaging
Sparsity

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Computer Science Applications
Human-Computer Interaction
Software
Electrical and Electronic Engineering
Atomic and Molecular Physics, and Optics

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10.2352/ISSN.2470-1173.2020.10.IPAS-179

Cite this

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title = "Hyperspectral complex-domain image denoising: cube complex-domain BM3D (CCDBM3D) algorithm",

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.",

keywords = "Block matching, Complex domain, Denoising, Hyperspectral imaging, Sparsity",

author = "Vladimir Katkovnik and Mykola Ponomarenko and Karen Egiazarian and Igor Shevkunov and Peter Kocsis",

note = "Funding Information: This work is supported by the Academy of Finland, project no. 138207, 2015-2019. Publisher Copyright: {\textcopyright} 2020, Society for Imaging Science and Technology Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; Image Processing: Algorithms and Systems Conference ; Conference date: 26-01-2020 Through 30-01-2020",

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doi = "10.2352/ISSN.2470-1173.2020.10.IPAS-179",

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TY - JOUR

T1 - Hyperspectral complex-domain image denoising

T2 - Image Processing: Algorithms and Systems Conference

AU - Katkovnik, Vladimir

AU - Ponomarenko, Mykola

AU - Egiazarian, Karen

AU - Shevkunov, Igor

AU - Kocsis, Peter

N1 - Funding Information: This work is supported by the Academy of Finland, project no. 138207, 2015-2019. Publisher Copyright: © 2020, Society for Imaging Science and Technology Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/1/26

Y1 - 2020/1/26

N2 - 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.

AB - 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.

KW - Block matching

KW - Complex domain

KW - Denoising

KW - Hyperspectral imaging

KW - Sparsity

U2 - 10.2352/ISSN.2470-1173.2020.10.IPAS-179

DO - 10.2352/ISSN.2470-1173.2020.10.IPAS-179

M3 - Conference article

AN - SCOPUS:85094879836

SN - 2470-1173

VL - 2020

JO - IS and T International Symposium on Electronic Imaging Science and Technology

JF - IS and T International Symposium on Electronic Imaging Science and Technology

IS - 10

Y2 - 26 January 2020 through 30 January 2020

ER -

Hyperspectral complex-domain image denoising: cube complex-domain BM3D (CCDBM3D) algorithm

Abstract

Keywords

Publication forum classification

ASJC Scopus subject areas

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