Exceeding the limits of algorithmic self-calibrated aberration recovery in Fourier ptychography

Abstract

Fourier ptychographic microscopy is a computational imaging technique that provides quantitative phase information and high resolution over a large field-of-view. Although the technique presents numerous advantages over conventional microscopy, model mismatch due to unknown optical aberrations can significantly limit reconstruction quality. A practical way of correcting for aberrations without additional data capture is through algorithmic self-calibration, in which a pupil recovery step is embedded into the reconstruction algorithm. However, software-only aberration correction is limited in accuracy. Here, we evaluate the merits of implementing a simple, dedicated calibration procedure for applications requiring high accuracy. In simulations, we find that for a target sample reconstruction error, we can image without any aberration corrections only up to a maximum aberration magnitude of λ/40. When we use algorithmic self-calibration, we can tolerate an aberration magnitude up to λ/10 and with our proposed diffuser calibration technique, this working range is extended further to λ/3. Hence, one can trade off complexity for accuracy by using a separate calibration process, which is particularly useful for larger aberrations.

High-resolution, large field-of-view label-free imaging via aberration-corrected, closed-form complex field reconstruction

“…This is problematic for exacting applications, such as digital pathology, where even small errors in the image are not tolerable. Furthermore, the joint optimization of aberration and sample spectrum can fail when the system’s aberrations are sufficiently severe—leading to poor reconstructions 16 . The iterative nature of FPM reconstruction algorithm has prompted researchers to adapt machine learning concepts to its implementation, in pursuit of computational load reduction, artifact abatement, and aberration correction 17 – 20 .…”