Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction
(2018) 26th European Signal Processing Conference, EUSIPCO 2018- 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 magnetoen-cephalography (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... (More)
- 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 magnetoen-cephalography (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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/51553074-348f-47e5-ade5-de5947ee0e04
- author
- Gao, Rui ; Tronarp, Filip LU and Särkkä, Simo
- publishing date
- 2018
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 26th European Signal Processing Conference (EUSIPCO)
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 26th European Signal Processing Conference, EUSIPCO 2018
- conference location
- Rome, Italy
- conference dates
- 2018-09-03 - 2018-09-07
- external identifiers
-
- scopus:85059804897
- ISBN
- 978-9-0827-9701-5
- 978-90-827970-0-8
- 978-1-5386-3736-4
- DOI
- 10.23919/EUSIPCO.2018.8553122
- language
- English
- LU publication?
- no
- id
- 51553074-348f-47e5-ade5-de5947ee0e04
- date added to LUP
- 2023-08-20 22:51:41
- date last changed
- 2024-05-04 14:29:51
@inproceedings{51553074-348f-47e5-ade5-de5947ee0e04, 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 magnetoen-cephalography (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.}}, author = {{Gao, Rui and Tronarp, Filip and Särkkä, Simo}}, booktitle = {{26th European Signal Processing Conference (EUSIPCO)}}, isbn = {{978-9-0827-9701-5}}, language = {{eng}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction}}, url = {{http://dx.doi.org/10.23919/EUSIPCO.2018.8553122}}, doi = {{10.23919/EUSIPCO.2018.8553122}}, year = {{2018}}, }