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Fourier ring correlation simplifies image restoration in fluorescence microscopy

Author

Listed:
  • Sami Koho

    (Istituto Italiano di Tecnologia
    University of Turku)

  • Giorgio Tortarolo

    (Istituto Italiano di Tecnologia
    University of Genoa)

  • Marco Castello

    (Istituto Italiano di Tecnologia)

  • Takahiro Deguchi

    (Istituto Italiano di Tecnologia)

  • Alberto Diaspro

    (Istituto Italiano di Tecnologia
    University of Genoa)

  • Giuseppe Vicidomini

    (Istituto Italiano di Tecnologia)

Abstract

Fourier ring correlation (FRC) has recently gained popularity among fluorescence microscopists as a straightforward and objective method to measure the effective image resolution. While the knowledge of the numeric resolution value is helpful in e.g., interpreting imaging results, much more practical use can be made of FRC analysis—in this article we propose blind image restoration methods enabled by it. We apply FRC to perform image de-noising by frequency domain filtering. We propose novel blind linear and non-linear image deconvolution methods that use FRC to estimate the effective point-spread-function, directly from the images. We show how FRC can be used as a powerful metric to observe the progress of iterative deconvolution. We also address two important limitations in FRC that may be of more general interest: how to make FRC work with single images (within certain practical limits) and with three-dimensional images with highly anisotropic resolution.

Suggested Citation

  • Sami Koho & Giorgio Tortarolo & Marco Castello & Takahiro Deguchi & Alberto Diaspro & Giuseppe Vicidomini, 2019. "Fourier ring correlation simplifies image restoration in fluorescence microscopy," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11024-z
    DOI: 10.1038/s41467-019-11024-z
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    Cited by:

    1. Yilun Li & Fang Huang, 2024. "A statistical resolution measure of fluorescence microscopy with finite photons," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Alessandro Rossetta & Eli Slenders & Mattia Donato & Sabrina Zappone & Francesco Fersini & Martina Bruno & Francesco Diotalevi & Luca Lanzanò & Sami Koho & Giorgio Tortarolo & Andrea Barberis & Marco , 2022. "The BrightEyes-TTM as an open-source time-tagging module for democratising single-photon microscopy," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Chupao Lin & Juan Santo Domingo Peñaranda & Jolien Dendooven & Christophe Detavernier & David Schaubroeck & Nico Boon & Roel Baets & Nicolas Le Thomas, 2022. "UV photonic integrated circuits for far-field structured illumination autofluorescence microscopy," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    4. Giorgio Tortarolo & Alessandro Zunino & Francesco Fersini & Marco Castello & Simonluca Piazza & Colin J. R. Sheppard & Paolo Bianchini & Alberto Diaspro & Sami Koho & Giuseppe Vicidomini, 2022. "Focus image scanning microscopy for sharp and gentle super-resolved microscopy," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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