Image restoration of undersampled two-photon microscopy with conditional generative adversarial network

Ryan K. Y. Chan; Hongsen He; W. L. So; Hiu Ka Fok; Yu-Xuan Ren; Cora S. W. Lai; Kenneth K. Y. Wong

doi:10.1364/CLEO_AT.2021.JW1A.50

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2021),
paper JW1A.50
•https://doi.org/10.1364/CLEO_AT.2021.JW1A.50

Image restoration of undersampled two-photon microscopy with conditional generative adversarial network

Ryan K. Y. Chan, Hongsen He, W. L. So, Hiu Ka Fok, Yu-Xuan Ren, Cora S. W. Lai, and Kenneth K. Y. Wong

CLEO: QELS_Fundamental Science 2021

San Jose, California United States
9–14 May 2021
ISBN: 978-1-943580-91-0

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Joint Poster Session II (JW1A)

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R. K. Y. Chan, H. He, W. L. So, H. K. Fok, Y. Ren, C. S. W. Lai, and K. K. Y. Wong, "Image restoration of undersampled two-photon microscopy with conditional generative adversarial network," in Conference on Lasers and Electro-Optics, J. Kang, S. Tomasulo, I. Ilev, D. Müller, N. Litchinitser, S. Polyakov, V. Podolskiy, J. Nunn, C. Dorrer, T. Fortier, Q. Gan, and C. Saraceno, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper JW1A.50.

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Abstract

We demonstrate the use of conditional generative adversarial network in restoring undersampled two-photon microscopic image. Image resolution and contrast can be substantially improved without noticeable artefacts with a 4-fold increase in imaging speed.

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