Fourier ptychographic microscopy (FPM) undergoes fast development since its proposal. In FPM, a large number of small images using small numerical aperture (NA) objective lens is generally required for the process of high-resolution image reconstruction. Although various methods have been proposed to shorten the acquisition time or reconstruct the image using sparse sample of full data, the scientific question is still there, i.e. where is the boundary of extreme sparse sampling for FPM, either from the theory or experimental perspective. In this paper, based on the in-house Incremental Inverse Dynamical Photon scattering (IIDPS) framework, we report that this artificial neural network based method, utilizing least absolute deviations error metric instead of the commonly used mean square error metric, is able to reconstruct image at very low sampling rate the simulation data for FPM, i.e. 5.67%, that is much lower than the reported results while other traditional method, like Gerchberg-Saxton-type method or other similar method, could not perform successful image reconstruction at such low sampling rate.
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