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PreprintCC BYbioRxiv

Maximum Likelihood Localization Maps

  • H. Zarei Oshtolagh
  • R. Foerster
  • J. Ries
  • R. Heintzmann1
  1. 1Leibniz Institute of Photonic Technology, Institute of Physical Chemistry, Frierich Schiller University Jena, Abbe Center of Photonics
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Abstract

Single-molecule localization microscopy commonly reconstructs images from estimated emitter coordinates. We calculate maximum-likelihood localization maps (MLLM), a non-iterative approach that evaluates positional likelihood from measured data and a supplied point-spread function. The MLLM of each detected single molecule image is added into a final upsampled image representing the overall molecular probability map, preserving the positional likelihood structure and obviating separate coordinate-based rendering. MLLM calculation is noniterative, fast, supports any calibrated point-spread function, accounts for local background variation, supports two- and three-dimensional reconstruction, and can be calculated in parallel.

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Cite

BibTeXRIS
BibTeX
@misc{oshtolagh2026maximum,
  title = {{Maximum Likelihood Localization Maps}},
  author = {H. Zarei Oshtolagh and R. Foerster and J. Ries and R. Heintzmann},
  howpublished = {bioRxiv},
  year = {2026},
  month = nov,
  doi = {10.64898/2026.11.05.764521},
  url = {https://www.biorxiv.org/content/10.64898/2026.11.05.764521v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Maximum Likelihood Localization Maps
AU  - H. Zarei Oshtolagh
AU  - R. Foerster
AU  - J. Ries
AU  - R. Heintzmann
PY  - 2026
DA  - 2026/11/05
PB  - bioRxiv
DO  - 10.64898/2026.11.05.764521
UR  - https://www.biorxiv.org/content/10.64898/2026.11.05.764521v1
AB  - Single-molecule localization microscopy commonly reconstructs images from estimated emitter coordinates. We calculate maximum-likelihood localization maps (MLLM), a non-iterative approach that evaluates positional likelihood from measured data and a supplied point-spread function. The MLLM of each detected single molecule image is added into a final upsampled image representing the overall molecular probability map, preserving the positional likelihood structure and obviating separate coordinate-based rendering. MLLM calculation is noniterative, fast, supports any calibrated point-spread function, accounts for local background variation, supports two- and three-dimensional reconstruction, and can be calculated in parallel.
ER  -