Biomedical Imaging Group
Logo EPFL
    • Splines Tutorials
    • Splines Art Gallery
    • Wavelets Tutorials
    • Image denoising
    • ERC project: FUN-SP
    • Sparse Processes - Book Preview
    • ERC project: GlobalBioIm
    • The colored revolution of bioimaging
    • Deconvolution
    • SMLM
    • One-World Seminars: Representer theorems
    • A Unifying Representer Theorem
Follow us on Twitter.
Join our Github.
Masquer le formulaire de recherche
Menu
BIOMEDICAL IMAGING GROUP (BIG)
Laboratoire d'imagerie biomédicale (LIB)
  1. School of Engineering STI
  2. Institute IEM
  3.  LIB
  4.  Micrograph Deconvolution
  • Laboratory
    • Laboratory
    • Laboratory
    • People
    • Jobs and Trainees
    • News
    • Events
    • Seminars
    • Resources (intranet)
    • Twitter
  • Research
    • Research
    • Researchs
    • Research Topics
    • Talks, Tutorials, and Reviews
  • Publications
    • Publications
    • Publications
    • Database of Publications
    • Talks, Tutorials, and Reviews
    • EPFL Infoscience
  • Code
    • Code
    • Code
    • Demos
    • Download Algorithms
    • Github
  • Teaching
    • Teaching
    • Teaching
    • Courses
    • Student projects
  • Splines
    • Teaching
    • Teaching
    • Splines Tutorials
    • Splines Art Gallery
    • Wavelets Tutorials
    • Image denoising
  • Sparsity
    • Teaching
    • Teaching
    • ERC project: FUN-SP
    • Sparse Processes - Book Preview
  • Imaging
    • Teaching
    • Teaching
    • ERC project: GlobalBioIm
    • The colored revolution of bioimaging
    • Deconvolution
    • SMLM
  • Machine Learning
    • Teaching
    • Teaching
    • One-World Seminars: Representer theorems
    • A Unifying Representer Theorem

High-Performance 3D Deconvolution of Fluorescence Micrographs

S. Kromwijk, S. Lefkimmiatis, M. Unser

Proceedings of the 2014 Twenty-First IEEE International Conference on Image Processing (ICIP'14), Paris, French Republic, October 27-30, 2014, pp. 1718-1722.


In this work, we describe our approach of combining the most effective ideas and tools developed during the past years to build a variational 3D deconvolution system that can be successfully employed in fluorescence microscopy. In particular, the main components of our deconvolution system involve proper handling of image boundaries, choice of a regularizer that is best suited to biological images, and use of an optimization algorithm that can be efficiently implemented on graphics processing units (GPUs) and fully benefit from their massive parallel computational capabilities. We show that our system leads to very competitive results and reduces the computational time by at least one order of magnitude compared to a CPU implementation. This makes the use of advanced deconvolution techniques feasible in practice and attractive computationally.

@INPROCEEDINGS(http://bigwww.epfl.ch/publications/kromwijk1401.html,
AUTHOR="Kromwijk, S. and Lefkimmiatis, S. and Unser, M.",
TITLE="High-Performance {3D} Deconvolution of Fluorescence Micrographs",
BOOKTITLE="Proceedings of the 2014 Twenty-First {IEEE} International
	Conference on Image Processing ({ICIP'14})",
YEAR="2014",
editor="",
volume="",
series="",
pages="1718--1722",
address="Paris, French Republic",
month="October 27-30,",
organization="",
publisher="",
note="")

© 2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
  • Laboratory
  • Research
  • Publications
    • Database of Publications
    • Talks, Tutorials, and Reviews
    • EPFL Infoscience
  • Code
  • Teaching
Logo EPFL, Ecole polytechnique fédérale de Lausanne
Emergencies: +41 21 693 3000 Services and resources Contact Map Webmaster email

Follow EPFL on social media

Follow us on Facebook. Follow us on Twitter. Follow us on Instagram. Follow us on Youtube. Follow us on LinkedIn.
Accessibility Disclaimer Privacy policy

© 2023 EPFL, all rights reserved