Richardson–Lucy Deconvolution as a General Tool for Combining Images with Complementary Strengths

Authors

  • Dr. Maria Ingaramo,

    1. Section on Biophotonics, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892 (USA), Fax: (+01) 301-496-6608
    Search for more papers by this author
  • Dr. Andrew G. York,

    1. Section on High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892 (USA)
    Search for more papers by this author
  • Eelco Hoogendoorn,

    1. Molecular Cytology, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam (The Netherlands)
    Search for more papers by this author
  • Dr. Marten Postma,

    1. Molecular Cytology, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam (The Netherlands)
    Search for more papers by this author
  • Dr. Hari Shroff,

    1. Section on High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892 (USA)
    Search for more papers by this author
  • Dr. George H. Patterson

    Corresponding author
    1. Section on Biophotonics, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892 (USA), Fax: (+01) 301-496-6608
    • Section on Biophotonics, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892 (USA), Fax: (+01) 301-496-6608

    Search for more papers by this author

Abstract

We use Richardson–Lucy (RL) deconvolution to combine multiple images of a simulated object into a single image in the context of modern fluorescence microscopy techniques. RL deconvolution can merge images with very different point-spread functions, such as in multiview light-sheet microscopes,1, 2 while preserving the best resolution information present in each image. We show that RL deconvolution is also easily applied to merge high-resolution, high-noise images with low-resolution, low-noise images, relevant when complementing conventional microscopy with localization microscopy. We also use RL deconvolution to merge images produced by different simulated illumination patterns, relevant to structured illumination microscopy (SIM)3, 4 and image scanning microscopy (ISM). The quality of our ISM reconstructions is at least as good as reconstructions using standard inversion algorithms for ISM data, but our method follows a simpler recipe that requires no mathematical insight. Finally, we apply RL deconvolution to merge a series of ten images with varying signal and resolution levels. This combination is relevant to gated stimulated-emission depletion (STED) microscopy, and shows that merges of high-quality images are possible even in cases for which a non-iterative inversion algorithm is unknown.

Ancillary