title: Experimental robustness of Fourier ptychography phase retrieval algorithms.
abstract: Fourier ptychography is a new computational microscopy technique that provides gigapixel-scale intensity and phase images with both wide field-of-view and high resolution. By capturing a stack of low-resolution images under different illumination angles, an inverse algorithm can be used to computationally reconstruct the high-resolution complex field. Here, we compare and classify multiple proposed inverse algorithms in terms of experimental robustness. We find that the main sources of error are noise, aberrations and mis-calibration (i.e. model mis-match). Using simulations and experiments, we demonstrate that the choice of cost function plays a critical role, with amplitude-based cost functions performing better than intensity-based ones. The reason for this is that Fourier ptychography datasets consist of images from both brightfield and darkfield illumination, representing a large range of measured intensities. Both noise (e.g. Poisson noise) and model mis-match errors are shown to scale with intensity. Hence, algorithms that use an appropriate cost function will be more tolerant to both noise and model mis-match. Given these insights, we propose a global Newton's method algorithm which is robust and accurate. Finally, we discuss the impact of procedures for algorithmic correction of aberrations and mis-calibration.
authors.forename: Li-Hao
authors.initials: LH
authors.lastname: Yeh
authors.forename: Jonathan
authors.initials: J
authors.lastname: Dong
authors.forename: Jingshan
authors.initials: J
authors.lastname: Zhong
authors.forename: Lei
authors.initials: L
authors.lastname: Tian
authors.forename: Michael
authors.initials: M
authors.lastname: Chen
authors.forename: Gongguo
authors.initials: G
authors.lastname: Tang
authors.forename: Mahdi
authors.initials: M
authors.lastname: Soltanolkotabi
authors.forename: Laura
authors.initials: L
authors.lastname: Waller