{"ID":23507302,"CreatedAt":"2026-09-18T02:21:44.056544415Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.20465","arxiv_id":"2609.20465","title":"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.","short_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.","url_abs":"https://arxiv.org/abs/2609.20465","url_pdf":"https://arxiv.org/pdf/2609.20465v1","authors":"[\"Yi Sun\",\"Mona Sharifi\",\"Muzna Yumman\"]","published":"2026-09-17T14:25:49Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}