Patch based low rank structured matrix completion for accelerated scanning microscopy

Kyong Hwan Jin; Junhong Min; Jong Chul Ye

doi:10.1109/ISBI.2015.7164097

Patch based low rank structured matrix completion for accelerated scanning microscopy

Kyong Hwan Jin
, Junhong Min
, Jong Chul Ye

Department of Electrical Engineering and Computer Science

Korea Advanced Institute of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

We propose a low rank structured matrix completion algorithm for image inpainting problems originated from scanning microscopy. The proposed method exploits the annihilation property observed in Gaussian Markov Random Field (GMRF) or partial differential equation (PDE)-based inpainting approaches. By utilizing the commutative property of the convolution, the annihilation property is embodied into rank-deficient block Hankel structure data matrices and the image inpainting problem is converted into low-rank structured matrix completion problem. To solve the structured low-rank matrix completion problem, an alternating direction method of multiplier (ADMM) method is used with factorization matrix initialization using the low rank matrix fitting (LMaFit) algorithm. Experimental results showed that the proposed method outperforms the existing state-of-the-art image inpainting methods.

Original language	English
Title of host publication	2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
Publisher	IEEE Computer Society
Pages	1236-1239
Number of pages	4
ISBN (Electronic)	9781479923748
DOIs	https://doi.org/10.1109/ISBI.2015.7164097
State	Published - 21 Jul 2015
Event	12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States Duration: 16 Apr 2015 → 19 Apr 2015

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2015-July
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Country/Territory	United States
City	Brooklyn
Period	16/04/15 → 19/04/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

ADMM
LMaFit
Scanning microscopy
low rank matrix completion
structured block Hankel matrix

Access to Document

10.1109/ISBI.2015.7164097

Cite this

Jin, K. H., Min, J., & Ye, J. C. (2015). Patch based low rank structured matrix completion for accelerated scanning microscopy. In 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015 (pp. 1236-1239). Article 7164097 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2015-July). IEEE Computer Society. https://doi.org/10.1109/ISBI.2015.7164097

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title = "Patch based low rank structured matrix completion for accelerated scanning microscopy",

abstract = "We propose a low rank structured matrix completion algorithm for image inpainting problems originated from scanning microscopy. The proposed method exploits the annihilation property observed in Gaussian Markov Random Field (GMRF) or partial differential equation (PDE)-based inpainting approaches. By utilizing the commutative property of the convolution, the annihilation property is embodied into rank-deficient block Hankel structure data matrices and the image inpainting problem is converted into low-rank structured matrix completion problem. To solve the structured low-rank matrix completion problem, an alternating direction method of multiplier (ADMM) method is used with factorization matrix initialization using the low rank matrix fitting (LMaFit) algorithm. Experimental results showed that the proposed method outperforms the existing state-of-the-art image inpainting methods.",

keywords = "ADMM, LMaFit, Scanning microscopy, low rank matrix completion, structured block Hankel matrix",

author = "Jin, \{Kyong Hwan\} and Junhong Min and Ye, \{Jong Chul\}",

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doi = "10.1109/ISBI.2015.7164097",

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Jin, KH, Min, J & Ye, JC 2015, Patch based low rank structured matrix completion for accelerated scanning microscopy. in 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015., 7164097, Proceedings - International Symposium on Biomedical Imaging, vol. 2015-July, IEEE Computer Society, pp. 1236-1239, 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, United States, 16/04/15. https://doi.org/10.1109/ISBI.2015.7164097

Patch based low rank structured matrix completion for accelerated scanning microscopy. / Jin, Kyong Hwan; Min, Junhong; Ye, Jong Chul.
2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015. IEEE Computer Society, 2015. p. 1236-1239 7164097 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2015-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Patch based low rank structured matrix completion for accelerated scanning microscopy

AU - Jin, Kyong Hwan

AU - Min, Junhong

AU - Ye, Jong Chul

PY - 2015/7/21

Y1 - 2015/7/21

N2 - We propose a low rank structured matrix completion algorithm for image inpainting problems originated from scanning microscopy. The proposed method exploits the annihilation property observed in Gaussian Markov Random Field (GMRF) or partial differential equation (PDE)-based inpainting approaches. By utilizing the commutative property of the convolution, the annihilation property is embodied into rank-deficient block Hankel structure data matrices and the image inpainting problem is converted into low-rank structured matrix completion problem. To solve the structured low-rank matrix completion problem, an alternating direction method of multiplier (ADMM) method is used with factorization matrix initialization using the low rank matrix fitting (LMaFit) algorithm. Experimental results showed that the proposed method outperforms the existing state-of-the-art image inpainting methods.

AB - We propose a low rank structured matrix completion algorithm for image inpainting problems originated from scanning microscopy. The proposed method exploits the annihilation property observed in Gaussian Markov Random Field (GMRF) or partial differential equation (PDE)-based inpainting approaches. By utilizing the commutative property of the convolution, the annihilation property is embodied into rank-deficient block Hankel structure data matrices and the image inpainting problem is converted into low-rank structured matrix completion problem. To solve the structured low-rank matrix completion problem, an alternating direction method of multiplier (ADMM) method is used with factorization matrix initialization using the low rank matrix fitting (LMaFit) algorithm. Experimental results showed that the proposed method outperforms the existing state-of-the-art image inpainting methods.

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KW - LMaFit

KW - Scanning microscopy

KW - low rank matrix completion

KW - structured block Hankel matrix

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M3 - Conference contribution

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T3 - Proceedings - International Symposium on Biomedical Imaging

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BT - 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015

PB - IEEE Computer Society

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Y2 - 16 April 2015 through 19 April 2015

ER -

Patch based low rank structured matrix completion for accelerated scanning microscopy

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