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Optimal 2D-SIM reconstruction by two filtering steps with Richardson-Lucy deconvolution.

ABSTRACT: Structured illumination microscopy relies on reconstruction algorithms to yield super-resolution images. Artifacts can arise in the reconstruction and affect the image quality. Current reconstruction methods involve a parametrized apodization function and a Wiener filter. Empirically tuning the parameters in these functions can minimize artifacts, but such an approach is subjective and produces volatile results. We present a robust and objective method that yields optimal results by two straightforward filtering steps with Richardson-Lucy-based deconvolutions. We provide a resource to identify artifacts in 2D-SIM images by analyzing two main reasons for artifacts, out-of-focus background and a fluctuating reconstruction spectrum. We show how the filtering steps improve images of test specimens, microtubules, yeast and mammalian cells.

SUBMITTER: Perez V

PROVIDER: S-EPMC5111067 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Optimal 2D-SIM reconstruction by two filtering steps with Richardson-Lucy deconvolution.

Perez Victor V Chang Bo-Jui BJ Stelzer Ernst Hans Karl EH

Scientific reports 20161116

Structured illumination microscopy relies on reconstruction algorithms to yield super-resolution images. Artifacts can arise in the reconstruction and affect the image quality. Current reconstruction methods involve a parametrized apodization function and a Wiener filter. Empirically tuning the parameters in these functions can minimize artifacts, but such an approach is subjective and produces volatile results. We present a robust and objective method that yields optimal results by two straight ...[more]

PMID: 27849043

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