VIPR: Vectorial Implementation of Phase Retrieval for fast and accurate microscopic pixel-wise pupil estimation

Abstract

In microscopy, proper modeling of the image formation has a substantial effect on the precision and accuracy in localization experiments and facilitates the correction of aberrations in adaptive optics experiments. The observed images are subject to polarization effects, refractive index variations and system specific constraints. Previously reported techniques have addressed these challenges by using complicated calibration samples, computationally heavy numerical algorithms, and various mathematical simplifications. In this work, we present a phase retrieval approach based on an analytical derivation of the vectorial diffraction model. Our method produces an accurate estimate of the system phase information (without any prior knowledge) in under a minute.

Practical sensorless aberration estimation for 3D microscopy with deep learning

“…This demonstrates that a PHASENET model trained only on synthetic images can indeed generalize to experimental data and achieve better performance than classical methods. Interestingly, although this dataset uses a high numerical aperture objective, PHASENET achieves high accuracy despite using only a scalar PSF model (2) which neglects vectorial effects in the PSF simulation [ 17 ]. Crucially, predictions with PHASENET were obtained orders of magnitude faster than with both GS and ZOLA (cf.…”

“…Classical approaches to phase retrieval include alternating projection methods such as Gerchberg-Saxton (GS) [ 11 , 15 ] or parameterized PSF fitting methods such as ZOLA [ 16 ] or VIPR [ 17 ]. While projection methods are typically fast but can perform poorly especially for noisy images, PSF fitting methods can achieve excellent results yet are relatively slow.…”

Resolving the 3D rotational and translational dynamics of single molecules using radially and azimuthally polarized fluorescence

“…Imaging system schematic and alignment and calibration procedures; characterization of raPol's detection and estimation performance; additional NR orientation-localization analysis; additional references. 64,65 The data underlying this study are openly available in OSF at https://osf.io/ 64bfv/?view_only=3a2cbdc3fd20467486f3574d54ecc7f6 and from the corresponding author upon reasonable request.…”

Learning an optimal PSF-pair for ultra-dense 3D localization microscopy

“…4 µms), we first generate a synthetic z-stack comprised of the approximate in-focus Airy disk PSF A (x, y) at 200 nm steps. Afterwards, we use stochastic gradient descent iterations with importance sampling [72] to recover the phase mask M associated with this PSF. Let D be the diffraction limit for the assumed optical setup.…”