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