Abstract

Extended-range three-dimensional (3D) single-molecule localization microscopy (SMLM) and single-particle tracking (SPT) require precise emitter localization across cellular-scale axial distances. However, long-rangeengineered point-spread functions (PSFs) spread photons over wider camera footprints, lowering the signal-to-noise ratio (SNR) and localization precision. We numerically evaluated a physics-guided deep learning workflow for 3D localization over a 10.0 µm axial range using simulated electron-multiplying charge-coupled device (EMCCD) images. The workflow combines an analytical secondary-astigmatism phase mask, frequency-domain cross-filtering, a cross-filtering generative adversarial network (CFGAN), and coarse-to-fine fitting. The optical model and engineered PSF provide physical signal priors, cross-filtering preserves directional Fourier-domain energy, and CFGAN suppresses residual structured noise before model-based localization. In low-SNR simulations, lateral, axial, and radial root-mean-squared localization errors (RMSEs) decreased from 54.11, 96.12, and 112.79 nm without denoising to 31.14, 39.06, and 50.12 nm after CFGAN denoising—close to Cramér–Rao lower-bound (CRLB) references of 34.39, 38.94, and 51.95 nm. High-SNR RMSE values were 8.78, 12.00, and 14.96 nm, comparable to CRLB references of 10.36, 11.71, and 15.64 nm. These simulations suggest that physics-guided restoration can improve extended-range 3D SMLM precision, while experimental validation remains necessary.

IPC Classification

Keywords