Experimental robustness of Fourier Ptychography phase retrieval algorithms

By Li-Hao Yeh, Jonathan Dong, Jingshan Zhong, Lei Tian, M. Chen, Gongguo Tang, M. Soltanolkotabi, Laura Waller. Optics Express, 2015

341 citations recorded by the source corpus.

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.

Subfields: Atomic and Molecular Physics, and Optics, Radiation, Structural Biology

View Paper: https://www.semanticscholar.org/paper/1d0ec20f6413c4058ee353b7270bf647ee7bb34b

How researchers see this paper

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In Atomic and Molecular Physics, and Optics

1. Yonina C. Eldar
2. Georgios B. Giannakis
3. G. Wang

In Radiation

1. Yonina C. Eldar
2. Georgios B. Giannakis
3. G. Wang

In Structural Biology

1. Yonina C. Eldar
2. Georgios B. Giannakis
3. G. Wang

Citations in the corpus

• Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow (2016, methodology): “This Paper Along the lines of suitably initialized nonconvex schemes [ 31], [6] and inspired by [53], the present paper develops a linear-time (in both dimensions m andn) algorithm to minimize the amplitude-based cost function, referred to as truncated amplitude flow(TAF).”