Exceeding the limits of algorithmic self-calibration in super-resolution imaging

TL;DR

This paper demonstrates that dedicated calibration procedures significantly outperform purely algorithmic self-calibration in Fourier ptychographic microscopy, enabling high-quality imaging even with large optical aberrations.

Contribution

The study shows that implementing simple calibration techniques extends the aberration correction range beyond algorithmic limits, improving super-resolution imaging.

Findings

Calibration extends aberration correction up to λ/3

Algorithmic self-calibration limits at λ/10

Purely algorithmic approach fails at large aberrations

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. Many attempts to address this issue rely on embedding pupil recovery into the reconstruction algorithm. In this paper we demonstrate the limitations of a purely algorithmic approach and evaluate the merits of implementing a simple, dedicated calibration procedure. In simulations, we find that for a target sample reconstruction error, we can image without any aberration corrections up to a maximum aberration magnitude of $\lambda$/40. When we use algorithmic self-calibration, we can increase the aberration magnitude up to $\lambda$/10, and with our in situ speckle calibration technique, this working range is extended further to a maximum aberration magnitude of $\lambda$/3. Hence, one can trade-off complexity for accuracy by using a separate calibration process, which is particularly useful for larger aberrations.