Presentation
2 March 2022 Deep residual network with data consistency for subsampled Fourier ptychographic microscopy
Author Affiliations +
Proceedings Volume PC11970, Quantitative Phase Imaging VIII; PC119700B (2022) https://doi.org/10.1117/12.2609572
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
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.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hyeon Gyu Kim, Kyung Won Kim, Kyung Chul Lee, Tae Joon Eo, Kyungwon Lee, Yohan Jun, Seung Ah Lee, and 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
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KEYWORDS
Data modeling

Microscopy

Performance modeling

Reconstruction algorithms

Phase measurement

Phase retrieval

Image retrieval

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