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Fourier Ptychographic Microscopy (FPM) is a computational imaging technique which reconstructs super-resolved amplitude and phase images by combining variably illuminated low-resolution images through an iterative phase retrieval algorithm. However, the phase-retrieval-based reconstruction requires sufficient overlap between spatial frequency bands of the measurements, which creates a trade-off between the number of measurements and the reconstruction quality. We propose a deep-learning-based FPM reconstruction that recovers both amplitude and phase images in high resolution with far fewer measurements than conventional FPM, with model-based constraint. Our model works with almost no overlap between low-resolution measurements in the Fourier domain, only taking into account the total Fourier extent of the measurements.
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Hyeon Gyu Kim, Kyung Won Kim, Kyung Chul Lee, Tae Joon Eo, Kyungwon Lee, Yohan Jun, Seung Ah Lee, Do Sik Hwang, "Deep residual network with data consistency for subsampled Fourier ptychographic microscopy," Proc. SPIE PC11970, Quantitative Phase Imaging VIII, PC119700B (2 March 2022); https://doi.org/10.1117/12.2609572