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Paper Abstract and Keywords
Presentation 2018-05-17 15:15
On OCT Volumetric Data Restoration via Hierarchical Sparsity and Hard Constraint
Shogo Muramatsu, Satoshi Nagayama, Samuel Choi (Niigata Univ.), Shunsuke Ono (Tokyo Institute of Tech.), Takeru Ota, Fumiaki Nin, Hiroshi Hibino (Niigata Univ.) SIP2018-3 IE2018-3 PRMU2018-3 MI2018-3
Abstract (in Japanese) (See Japanese page) 
(in English) This work proposes a novel restoration method for optical coherence tomography (OCT) data. OCT is a measurement technique that acquires a tomographic image in the order of a few $mu$m scale by using a near infrared laser. In order to deeply understand the mechanism of sensory epithelium in cochlear, the authors are working on dynamic tomographic imaging of hair cells of guinea pig in vivo and develop a multi-frequency swept (MS) enface OCT device. Currently, the device faces to a problem that the reflected light is quite weak, and a help of image processing is demanded to estimate the reflectional distribution hidden in noise. In the article cite{icassp2018}, a restoration method is given by reducing the problem to a regularized least squares one with a hard constraint for reflections. This work further proposes to introduce another regularization and constraint for the latent refractive index distribution. The significance of the proposed model is verified by simulation on artificial data.
Keyword (in Japanese) (See Japanese page) 
(in English) Volumetric data restoration / Primal-dual proximal splitting method / Sparse regularization / Hard constraint / BM4D / OCT / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 33, SIP2018-3, pp. 7-12, May 2018.
Paper # SIP2018-3 
Date of Issue 2018-05-10 (SIP, IE, PRMU, MI) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (No. 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SIP2018-3 IE2018-3 PRMU2018-3 MI2018-3

Conference Information
Committee PRMU MI IE SIP  
Conference Date 2018-05-17 - 2018-05-18 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
Conference Code 2018-05-PRMU-MI-IE-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) On OCT Volumetric Data Restoration via Hierarchical Sparsity and Hard Constraint 
Sub Title (in English)  
Keyword(1) Volumetric data restoration  
Keyword(2) Primal-dual proximal splitting method  
Keyword(3) Sparse regularization  
Keyword(4) Hard constraint  
Keyword(5) BM4D  
Keyword(6) OCT  
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Keyword(8)  
1st Author's Name Shogo Muramatsu  
1st Author's Affiliation Niigata University (Niigata Univ.)
2nd Author's Name Satoshi Nagayama  
2nd Author's Affiliation Niigata University (Niigata Univ.)
3rd Author's Name Samuel Choi  
3rd Author's Affiliation Niigata University (Niigata Univ.)
4th Author's Name Shunsuke Ono  
4th Author's Affiliation Tokyo Institute of Technology (Tokyo Institute of Tech.)
5th Author's Name Takeru Ota  
5th Author's Affiliation Niigata University (Niigata Univ.)
6th Author's Name Fumiaki Nin  
6th Author's Affiliation Niigata University (Niigata Univ.)
7th Author's Name Hiroshi Hibino  
7th Author's Affiliation Niigata University (Niigata Univ.)
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Speaker
Date Time 2018-05-17 15:15:00 
Presentation Time 30 
Registration for SIP 
Paper # IEICE-SIP2018-3,IEICE-IE2018-3,IEICE-PRMU2018-3,IEICE-MI2018-3 
Volume (vol) IEICE-118 
Number (no) no.33(SIP), no.34(IE), no.35(PRMU), no.36(MI) 
Page pp.7-12 
#Pages IEICE-6 
Date of Issue IEICE-SIP-2018-05-10,IEICE-IE-2018-05-10,IEICE-PRMU-2018-05-10,IEICE-MI-2018-05-10 


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