aiqbench takeMachine Learning

This research demonstrates how training neural networks on synthesized data can approximate the optimal Bayes estimator for dense emitter localization, advancing high-throughput super-resolution microscopy techniques. By bridging machine learning with physical imaging constraints, the study provides a rigorous theoretical justification for future large-field-of-view applications in single-molecule localization microscopy (SMLM).

Source coveringarXiv

What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Training Neural Networks to Approach the Optimum Bayes Estimator in Dense Multi-Emitter Localization

arXivSeptember 18, 20261 min read

arXiv:2609.20465v1 Announce Type: new Abstract: We train neural networks on synthesized frames to approach the optimum Bayes estimator for dense emitter localization. The result justifies the future work on training neural networks to achieve high-throughput large-FOV super spatiotemporal resolution SMLM.

Abstract:We train neural networks on synthesized frames to approach the optimum Bayes estimator for dense emitter localization. The result justifies the future work on training neural networks to achieve high-throughput large-FOV super spatiotemporal resolution SMLM.

Machine Learning (cs.LG)

arXiv:2609.20465 [cs.LG]

(or arXiv:2609.20465v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.20465 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Full text under the publisher's open licence · 129 words

Read at arXiv ↗Ask about this article