Hyperspectral phase imaging based on denoising in complex-valued eigensubspace

Igor Shevkunov; Vladimir Katkovnik; Daniel Claus; Giancarlo Pedrini; Nikolay V. Petrov; Karen Egiazarian

doi:10.1016/j.optlaseng.2019.105973

Hyperspectral phase imaging based on denoising in complex-valued eigensubspace

Igor Shevkunov, Vladimir Katkovnik, Daniel Claus, Giancarlo Pedrini, Nikolay V. Petrov, Karen Egiazarian

Computing Sciences

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Abstract

A novel algorithm for reconstruction of hyperspectral 3D complex domain images (phase/amplitude) from noisy complex domain observations has been developed and studied. This algorithm starts from the SVD (singular value decomposition) analysis of the observed complex-valued data and looks for the optimal low dimension eigenspace. These eigenspace images are processed based on special non-local block-matching complex domain filters. The accuracy and quantitative advantage of the new algorithm for phase and amplitude imaging are demonstrated in simulation tests and in processing of the experimental data. It is shown that the algorithm is effective and provides reliable results even for highly noisy data.

Original language	English
Article number	105973
Number of pages	10
Journal	Optics and Lasers in Engineering
Volume	127
Early online date	6 Dec 2019
DOIs	https://doi.org/10.1016/j.optlaseng.2019.105973
Publication status	Published - 1 Apr 2020
Publication type	A1 Journal article-refereed

Keywords

Hyperspectral imaging
Noise filtering
Noise in imaging systems
Phase imaging
Singular value decomposition
Sparse representation

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Mechanical Engineering
Electrical and Electronic Engineering

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10.1016/j.optlaseng.2019.105973

1-s2.0-S0143816619313491-mainFinal published version, 3.2 MBLicence: CC BY-NC-ND

http://urn.fi/URN:NBN:fi:tuni-202001101169Licence: CC BY-NC-ND

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title = "Hyperspectral phase imaging based on denoising in complex-valued eigensubspace",

abstract = "A novel algorithm for reconstruction of hyperspectral 3D complex domain images (phase/amplitude) from noisy complex domain observations has been developed and studied. This algorithm starts from the SVD (singular value decomposition) analysis of the observed complex-valued data and looks for the optimal low dimension eigenspace. These eigenspace images are processed based on special non-local block-matching complex domain filters. The accuracy and quantitative advantage of the new algorithm for phase and amplitude imaging are demonstrated in simulation tests and in processing of the experimental data. It is shown that the algorithm is effective and provides reliable results even for highly noisy data.",

keywords = "Hyperspectral imaging, Noise filtering, Noise in imaging systems, Phase imaging, Singular value decomposition, Sparse representation",

author = "Igor Shevkunov and Vladimir Katkovnik and Daniel Claus and Giancarlo Pedrini and Petrov, {Nikolay V.} and Karen Egiazarian",

year = "2020",

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AU - Shevkunov, Igor

AU - Katkovnik, Vladimir

AU - Claus, Daniel

AU - Pedrini, Giancarlo

AU - Petrov, Nikolay V.

AU - Egiazarian, Karen

PY - 2020/4/1

Y1 - 2020/4/1

N2 - A novel algorithm for reconstruction of hyperspectral 3D complex domain images (phase/amplitude) from noisy complex domain observations has been developed and studied. This algorithm starts from the SVD (singular value decomposition) analysis of the observed complex-valued data and looks for the optimal low dimension eigenspace. These eigenspace images are processed based on special non-local block-matching complex domain filters. The accuracy and quantitative advantage of the new algorithm for phase and amplitude imaging are demonstrated in simulation tests and in processing of the experimental data. It is shown that the algorithm is effective and provides reliable results even for highly noisy data.

AB - A novel algorithm for reconstruction of hyperspectral 3D complex domain images (phase/amplitude) from noisy complex domain observations has been developed and studied. This algorithm starts from the SVD (singular value decomposition) analysis of the observed complex-valued data and looks for the optimal low dimension eigenspace. These eigenspace images are processed based on special non-local block-matching complex domain filters. The accuracy and quantitative advantage of the new algorithm for phase and amplitude imaging are demonstrated in simulation tests and in processing of the experimental data. It is shown that the algorithm is effective and provides reliable results even for highly noisy data.

KW - Hyperspectral imaging

KW - Noise filtering

KW - Noise in imaging systems

KW - Phase imaging

KW - Singular value decomposition

KW - Sparse representation

U2 - 10.1016/j.optlaseng.2019.105973

DO - 10.1016/j.optlaseng.2019.105973

M3 - Article

AN - SCOPUS:85076060273

SN - 0143-8166

VL - 127

JO - Optics and Lasers in Engineering

JF - Optics and Lasers in Engineering

M1 - 105973

ER -

Hyperspectral phase imaging based on denoising in complex-valued eigensubspace

Abstract

Keywords

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ASJC Scopus subject areas

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