Enhancing fluorescence microscopy performance with Richardson-Lucy based deep learning

Yue Li; Yijun Su; Huafeng Liu; Yicong Wu; Hari Shroff

doi:10.1364/3D.2022.JW5B.3

Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP)
Technical Digest Series (Optica Publishing Group, 2022),
paper JW5B.3
•https://doi.org/10.1364/3D.2022.JW5B.3

Enhancing fluorescence microscopy performance with Richardson-Lucy based deep learning

Yue Li, Yijun Su, Huafeng Liu, Yicong Wu, and Hari Shroff

3D Image Acquisition and Display: Technology, Perception and Applications 2022

Vancouver, British Columbia Canada
11–15 July 2022
ISBN: 978-1-957171-09-8

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Joint Postdeadline Paper Session II (JW5B)

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Y. Li, Y. Su, H. Liu, Y. Wu, and H. Shroff, "Enhancing fluorescence microscopy performance with Richardson-Lucy based deep learning," in Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP), Technical Digest Series (Optica Publishing Group, 2022), paper JW5B.3.

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Abstract

We present a deep learning method which incorporates the Richardson-Lucy formula into a fully convolutional network and demonstrate its performance on cells, tissues, and embryos imaged with widefield-, confocal- light sheet-, and super-resolution microscopy.

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