Deep residual network with data consistency for subsampled Fourier ptychographic microscopy
23 January 2022 • 9:40 AM - 10:00 AM PST | Room 202 (Level 2 South)
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.
Yonsei Univ. (Korea, Republic of)
HyeonGyu Kim received the B.S. degree in electrical and electronic engineering from Yonsei University, Seoul, Korea, in 2020. He is currently pursuing the Ph.D. degree in electrical and electronic engineering at Yonsei University, Seoul, Korea. His research interests include FPM, MRI, image reconstruction, medical imaging, and deep learning.