Training Neural Networks to Approach the Optimum Bayes Estimator in Dense Multi-Emitter Localization
cs.LG
Submitted: 2026-09-17
Updated: 2026-09-17
Comments: 28 pages, 5 figures
Code: https://github.com/SunCCNY/Nanoscopy
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: We train neural networks on synthesized frames to approach the optimum Bayes estimator for dense emitter localization.
Terminology
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.
Sources
- Asymptotic Efficiency of the Global Maximum-Likelihood Estimator in Multi-Emitter Localization Microscopy
- Learning Activation Functions to Improve Deep Neural Networks
- KAN: Kolmogorov-Arnold Networks
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Adam: A Method for Stochastic Optimization
- SGDR: Stochastic Gradient Descent with Warm Restarts
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