Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction

Rui Gao; Filip Tronarp; Simo Särkkä

doi:10.23919/EUSIPCO.2018.8553122

Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction

Rui Gao^*, Filip Tronarp, Simo Särkkä

^*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

7 Sitaatiot (Scopus)

Abstrakti

In this article, we propose an efficient method for solving analysis-l1-TV regularization problems with a multi-step alternating direction method of multipliers (ADMM) approach as the fast solver. Additionally, we apply it to a real-data magnetoencephalography (MEG) brain imaging problem as well as to signal reconstruction. In our approach, the inverse problem arising in MEG or signal reconstruction is formulated as an optimization problem which we regularize using a combination of analysis-l1 prior together with a total variation (TV) regularization term. We then formulate an optimization algorithm based on ADMM which can effectively be used to solve the optimization problems. The performance of the algorithm is illustrated in practical scenarios.

Alkuperäiskieli	Englanti
Otsikko	Proceedings of the 26th European Signal Processing Conference, EUSIPCO 2018
Kustantaja	IEEE
Sivut	1930-1934
Sivumäärä	5
ISBN (painettu)	978-90-827970-1-5
DOI - pysyväislinkit	https://doi.org/10.23919/EUSIPCO.2018.8553122
Tila	Julkaistu - 2018
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	European Signal Processing Conference - Rome, Italia Kesto: 3 syysk. 2018 → 7 syysk. 2018 Konferenssinumero: 26

Julkaisusarja

Nimi	European Signal Processing Conference
Kustantaja	IEEE COMPUTER SOC
ISSN (painettu)	2076-1465

Conference

Conference	European Signal Processing Conference
Lyhennettä	EUSIPCO
Maa/Alue	Italia
Kaupunki	Rome
Ajanjakso	03/09/2018 → 07/09/2018

Pääsy asiakirjaan

10.23919/EUSIPCO.2018.8553122

Siteeraa tätä

@inproceedings{b2785a81aa6c402489fca5439599ed68,

title = "Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction",

abstract = "In this article, we propose an efficient method for solving analysis-l1-TV regularization problems with a multi-step alternating direction method of multipliers (ADMM) approach as the fast solver. Additionally, we apply it to a real-data magnetoencephalography (MEG) brain imaging problem as well as to signal reconstruction. In our approach, the inverse problem arising in MEG or signal reconstruction is formulated as an optimization problem which we regularize using a combination of analysis-l1 prior together with a total variation (TV) regularization term. We then formulate an optimization algorithm based on ADMM which can effectively be used to solve the optimization problems. The performance of the algorithm is illustrated in practical scenarios.",

keywords = "Analysis-l1-TV-norm, total variation (TV), alternating direction method of multipliers (ADMM), magnetoencephalography (MEG), image reconstruction, algorithm",

author = "Rui Gao and Filip Tronarp and Simo S{\"a}rkk{\"a}",

year = "2018",

doi = "10.23919/EUSIPCO.2018.8553122",

language = "English",

isbn = "978-90-827970-1-5",

series = "European Signal Processing Conference",

publisher = "IEEE",

pages = "1930--1934",

booktitle = "Proceedings of the 26th European Signal Processing Conference, EUSIPCO 2018",

address = "United States",

note = "European Signal Processing Conference, EUSIPCO ; Conference date: 03-09-2018 Through 07-09-2018",

}

Gao, R, Tronarp, F & Särkkä, S 2018, Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction. julkaisussa Proceedings of the 26th European Signal Processing Conference, EUSIPCO 2018. European Signal Processing Conference, IEEE, Sivut 1930-1934, European Signal Processing Conference, Rome, Italia, 03/09/2018. https://doi.org/10.23919/EUSIPCO.2018.8553122

Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction. / Gao, Rui; Tronarp, Filip; Särkkä, Simo.
Proceedings of the 26th European Signal Processing Conference, EUSIPCO 2018. IEEE, 2018. s. 1930-1934 (European Signal Processing Conference).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction

AU - Gao, Rui

AU - Tronarp, Filip

AU - Särkkä, Simo

N1 - Conference code: 26

PY - 2018

Y1 - 2018

N2 - In this article, we propose an efficient method for solving analysis-l1-TV regularization problems with a multi-step alternating direction method of multipliers (ADMM) approach as the fast solver. Additionally, we apply it to a real-data magnetoencephalography (MEG) brain imaging problem as well as to signal reconstruction. In our approach, the inverse problem arising in MEG or signal reconstruction is formulated as an optimization problem which we regularize using a combination of analysis-l1 prior together with a total variation (TV) regularization term. We then formulate an optimization algorithm based on ADMM which can effectively be used to solve the optimization problems. The performance of the algorithm is illustrated in practical scenarios.

AB - In this article, we propose an efficient method for solving analysis-l1-TV regularization problems with a multi-step alternating direction method of multipliers (ADMM) approach as the fast solver. Additionally, we apply it to a real-data magnetoencephalography (MEG) brain imaging problem as well as to signal reconstruction. In our approach, the inverse problem arising in MEG or signal reconstruction is formulated as an optimization problem which we regularize using a combination of analysis-l1 prior together with a total variation (TV) regularization term. We then formulate an optimization algorithm based on ADMM which can effectively be used to solve the optimization problems. The performance of the algorithm is illustrated in practical scenarios.

KW - Analysis-l1-TV-norm

KW - total variation (TV)

KW - alternating direction method of multipliers (ADMM)

KW - magnetoencephalography (MEG)

KW - image reconstruction

KW - algorithm

U2 - 10.23919/EUSIPCO.2018.8553122

DO - 10.23919/EUSIPCO.2018.8553122

M3 - Conference article in proceedings

SN - 978-90-827970-1-5

T3 - European Signal Processing Conference

SP - 1930

EP - 1934

BT - Proceedings of the 26th European Signal Processing Conference, EUSIPCO 2018

PB - IEEE

T2 - European Signal Processing Conference

Y2 - 3 September 2018 through 7 September 2018

ER -

Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction

Abstrakti

Julkaisusarja

Conference

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