A Non-convex Nonseparable Approach to Single-Molecule Localization Microscopy

Raymond H. CHAN; Damiana LAZZARO; Serena MORIGI; Fiorella SGALLARI

doi:10.1007/978-3-030-22368-7_39

A Non-convex Nonseparable Approach to Single-Molecule Localization Microscopy

Raymond H. CHAN
, Damiana LAZZARO
, Serena MORIGI^*
, Fiorella SGALLARI

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

1 Citation (Scopus)

Abstract

We present a method for high-density super-resolution microscopy which integrates a sparsity-promoting penalty and a blur kernel correction into a nonsmooth, non-convex, nonseparable variational formulation. An efficient majorization minimization strategy is applied to reduce the challenging optimization problem to the solution of a series of easier convex problems.

Original language	English
Title of host publication	Scale Space and Variational Methods in Computer Vision: 7th International Conference, SSVM 2019, Proceedings
Editors	Jan LELLMANN, Jan MODERSITZKI, Martin BURGER
Publisher	Springer, Cham
Pages	498-509
Number of pages	12
ISBN (Print)	9783030223670
DOIs	https://doi.org/10.1007/978-3-030-22368-7_39
Publication status	Published - 2019
Externally published	Yes
Event	7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019 - Hofgeismar, Germany Duration: 30 Jun 2019 → 4 Jul 2019

Publication series

Name	Lecture Notes in Computer Science
Volume	11603
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019
Country/Territory	Germany
City	Hofgeismar
Period	30/06/19 → 4/07/19

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Access to Document

10.1007/978-3-030-22368-7_39

Cite this

CHAN, R. H., LAZZARO, D., MORIGI, S., & SGALLARI, F. (2019). A Non-convex Nonseparable Approach to Single-Molecule Localization Microscopy. In J. LELLMANN, J. MODERSITZKI, & M. BURGER (Eds.), Scale Space and Variational Methods in Computer Vision: 7th International Conference, SSVM 2019, Proceedings (pp. 498-509). (Lecture Notes in Computer Science; Vol. 11603). Springer, Cham. https://doi.org/10.1007/978-3-030-22368-7_39

CHAN, Raymond H. ; LAZZARO, Damiana ; MORIGI, Serena et al. / A Non-convex Nonseparable Approach to Single-Molecule Localization Microscopy. Scale Space and Variational Methods in Computer Vision: 7th International Conference, SSVM 2019, Proceedings. editor / Jan LELLMANN ; Jan MODERSITZKI ; Martin BURGER. Springer, Cham, 2019. pp. 498-509 (Lecture Notes in Computer Science).

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title = "A Non-convex Nonseparable Approach to Single-Molecule Localization Microscopy",

abstract = "We present a method for high-density super-resolution microscopy which integrates a sparsity-promoting penalty and a blur kernel correction into a nonsmooth, non-convex, nonseparable variational formulation. An efficient majorization minimization strategy is applied to reduce the challenging optimization problem to the solution of a series of easier convex problems.",

author = "CHAN, \{Raymond H.\} and Damiana LAZZARO and Serena MORIGI and Fiorella SGALLARI",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019 ; Conference date: 30-06-2019 Through 04-07-2019",

year = "2019",

doi = "10.1007/978-3-030-22368-7\_39",

language = "English",

isbn = "9783030223670",

series = "Lecture Notes in Computer Science",

publisher = "Springer, Cham",

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CHAN, RH, LAZZARO, D, MORIGI, S & SGALLARI, F 2019, A Non-convex Nonseparable Approach to Single-Molecule Localization Microscopy. in J LELLMANN, J MODERSITZKI & M BURGER (eds), Scale Space and Variational Methods in Computer Vision: 7th International Conference, SSVM 2019, Proceedings. Lecture Notes in Computer Science, vol. 11603, Springer, Cham, pp. 498-509, 7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019, Hofgeismar, Germany, 30/06/19. https://doi.org/10.1007/978-3-030-22368-7_39

A Non-convex Nonseparable Approach to Single-Molecule Localization Microscopy. / CHAN, Raymond H.; LAZZARO, Damiana; MORIGI, Serena et al.
Scale Space and Variational Methods in Computer Vision: 7th International Conference, SSVM 2019, Proceedings. ed. / Jan LELLMANN; Jan MODERSITZKI; Martin BURGER. Springer, Cham, 2019. p. 498-509 (Lecture Notes in Computer Science; Vol. 11603).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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AU - SGALLARI, Fiorella

PY - 2019

Y1 - 2019

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AB - We present a method for high-density super-resolution microscopy which integrates a sparsity-promoting penalty and a blur kernel correction into a nonsmooth, non-convex, nonseparable variational formulation. An efficient majorization minimization strategy is applied to reduce the challenging optimization problem to the solution of a series of easier convex problems.

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DO - 10.1007/978-3-030-22368-7_39

M3 - Conference paper (refereed)

AN - SCOPUS:85068444587

SN - 9783030223670

T3 - Lecture Notes in Computer Science

SP - 498

EP - 509

BT - Scale Space and Variational Methods in Computer Vision: 7th International Conference, SSVM 2019, Proceedings

A2 - LELLMANN, Jan

A2 - MODERSITZKI, Jan

A2 - BURGER, Martin

PB - Springer, Cham

T2 - 7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019

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CHAN RH, LAZZARO D, MORIGI S, SGALLARI F. A Non-convex Nonseparable Approach to Single-Molecule Localization Microscopy. In LELLMANN J, MODERSITZKI J, BURGER M, editors, Scale Space and Variational Methods in Computer Vision: 7th International Conference, SSVM 2019, Proceedings. Springer, Cham. 2019. p. 498-509. (Lecture Notes in Computer Science). Epub 2019 Jun 5. doi: 10.1007/978-3-030-22368-7_39

A Non-convex Nonseparable Approach to Single-Molecule Localization Microscopy

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