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Sparse approximations in complex domain based on BM3D modeling

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Sparse approximations in complex domain based on BM3D modeling. / Katkovnik, Vladimir; Ponomarenko, Mykola; Egiazarian, Karen.

In: Signal Processing, Vol. 141, 01.12.2017, p. 96-108.

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@article{c6637d59980f472a855441008c88fcb9,
title = "Sparse approximations in complex domain based on BM3D modeling",
abstract = "In this paper the concept of sparsity for complex-valued variables is introduced in the following three types: directly in complex domain and for two real-valued pairs phase/amplitude and real/imaginary parts of complex variables. The nonlocal block-matching technique is used for sparsity implementation and filter design for each type of sparsity. These filters are complex domain generalizations of the Block Matching 3D collaborative (BM3D) filter based on the high-order singular value decomposition (HOSVD) in order to generate group-wise adaptive analysis/synthesis transforms. Complex domain denoising is developed and studied as a test-problem for comparison of the designed filters as well as the different types of sparsity modeling.",
keywords = "Block matching, Complex domain, Denoising, Elsevier article, Phase imaging, Sample document, Sparsity",
author = "Vladimir Katkovnik and Mykola Ponomarenko and Karen Egiazarian",
year = "2017",
month = "12",
day = "1",
doi = "10.1016/j.sigpro.2017.05.032",
language = "English",
volume = "141",
pages = "96--108",
journal = "Signal Processing",
issn = "0165-1684",
publisher = "Elsevier",

}

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TY - JOUR

T1 - Sparse approximations in complex domain based on BM3D modeling

AU - Katkovnik, Vladimir

AU - Ponomarenko, Mykola

AU - Egiazarian, Karen

PY - 2017/12/1

Y1 - 2017/12/1

N2 - In this paper the concept of sparsity for complex-valued variables is introduced in the following three types: directly in complex domain and for two real-valued pairs phase/amplitude and real/imaginary parts of complex variables. The nonlocal block-matching technique is used for sparsity implementation and filter design for each type of sparsity. These filters are complex domain generalizations of the Block Matching 3D collaborative (BM3D) filter based on the high-order singular value decomposition (HOSVD) in order to generate group-wise adaptive analysis/synthesis transforms. Complex domain denoising is developed and studied as a test-problem for comparison of the designed filters as well as the different types of sparsity modeling.

AB - In this paper the concept of sparsity for complex-valued variables is introduced in the following three types: directly in complex domain and for two real-valued pairs phase/amplitude and real/imaginary parts of complex variables. The nonlocal block-matching technique is used for sparsity implementation and filter design for each type of sparsity. These filters are complex domain generalizations of the Block Matching 3D collaborative (BM3D) filter based on the high-order singular value decomposition (HOSVD) in order to generate group-wise adaptive analysis/synthesis transforms. Complex domain denoising is developed and studied as a test-problem for comparison of the designed filters as well as the different types of sparsity modeling.

KW - Block matching

KW - Complex domain

KW - Denoising

KW - Elsevier article

KW - Phase imaging

KW - Sample document

KW - Sparsity

U2 - 10.1016/j.sigpro.2017.05.032

DO - 10.1016/j.sigpro.2017.05.032

M3 - Article

VL - 141

SP - 96

EP - 108

JO - Signal Processing

JF - Signal Processing

SN - 0165-1684

ER -