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

2026-09-17

AI Summary

研究通过训练神经网络处理合成帧来逼近最优贝叶斯估计器，以解决密集发射体定位问题，为实现高通量大视场超时空分辨率SMLM奠定基础。

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.

Problem

• Neural Networks
• Bayes Estimator
• Dense Emitter Localization

Innovation

• Neural Networks
• Bayes Estimator
• Dense Emitter Localization
• SMLM