2020
DOI: 10.48550/arxiv.2009.14303
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Learning an optimal PSF-pair for ultra-dense 3D localization microscopy

Elias Nehme,
Boris Ferdman,
Lucien E. Weiss
et al.

Abstract: A long-standing challenge in multiple-particle-tracking is the accurate and precise 3D localization of individual particles at close proximity. One established approach for snapshot 3D imaging is point-spread-function (PSF) engineering, in which the PSF is modified to encode the axial information. However, engineered PSFs are challenging to localize at high densities due to lateral PSF overlaps. Here we suggest using multiple PSFs simultaneously to help overcome this challenge, and investigate the problem of e… Show more

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Cited by 2 publications
(2 citation statements)
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“…7 Deep-learning methods have also been successfully applied to optimize paired PSFs to achieve precise 3D localization at high densities. 43 Similar machinelearning approaches with the single-spot RPSF such as that employed here can also potentially improve its error performance even under lower SNR and higher emitter-density conditions.…”
Section: Spp Localization Performancementioning
confidence: 95%
“…7 Deep-learning methods have also been successfully applied to optimize paired PSFs to achieve precise 3D localization at high densities. 43 Similar machinelearning approaches with the single-spot RPSF such as that employed here can also potentially improve its error performance even under lower SNR and higher emitter-density conditions.…”
Section: Spp Localization Performancementioning
confidence: 95%
“…In addition, with the development of super-resolution microscopy techniques mentioned above, the method to improve precision of locating a single emitter is also important. Efforts along this line include designing optimal point spread functions (PSF) [41,42] and the quantumlimited longitudinal localisation of a single emitter [43].…”
Section: Introductionmentioning
confidence: 99%