12 September 2017 System calibration method for Fourier ptychographic microscopy
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J. of Biomedical Optics, 22(9), 096005 (2017). doi:10.1117/1.JBO.22.9.096005
Fourier ptychographic microscopy (FPM) is a recently proposed computational imaging technique with both high-resolution and wide field of view. In current FPM imaging platforms, systematic error sources come from aberrations, light-emitting diode (LED) intensity fluctuation, parameter imperfections, and noise, all of which may severely corrupt the reconstruction results with similar artifacts. Therefore, it would be unlikely to distinguish the dominating error from these degraded reconstructions without any preknowledge. In addition, systematic error is generally a mixture of various error sources in the real situation, and it cannot be separated due to their mutual restriction and conversion. To this end, we report a system calibration procedure, termed SC-FPM, to calibrate the mixed systematic errors simultaneously from an overall perspective, based on the simulated annealing algorithm, the LED intensity correction method, the nonlinear regression process, and the adaptive step-size strategy, which involves the evaluation of an error metric at each iteration step, followed by the re-estimation of accurate parameters. The performance achieved both in simulations and experiments demonstrates that the proposed method outperforms other state-of-the-art algorithms. The reported system calibration scheme improves the robustness of FPM, relaxes the experiment conditions, and does not require any preknowledge, which makes the FPM more pragmatic.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
An Pan, Yan Zhang, Tianyu Zhao, Zhaojun Wang, Dan Dan, Ming Lei, Baoli Yao, "System calibration method for Fourier ptychographic microscopy," Journal of Biomedical Optics 22(9), 096005 (12 September 2017). https://doi.org/10.1117/1.JBO.22.9.096005 Submission: Received 24 June 2017; Accepted 21 August 2017
Submission: Received 24 June 2017; Accepted 21 August 2017

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