Journal
APPLIED OPTICS
Volume 62, Issue 15, Pages 3989-3999Publisher
Optica Publishing Group
DOI: 10.1364/AO.482788
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Multispectral quantitative phase imaging (MS-QPI) is achieved by using a highly spatially sensitive digital holographic microscope assisted by a deep neural network to extract spectral dependent quantitative information in single-shot. Three different wavelengths (532, 633, and 808 nm) are used, and interferometric data is acquired for each wavelength. A generative adversarial network is trained to generate multispectral (MS) quantitative phase maps from a single input interferogram. The validation of the approach is done by comparing the predicted MS phase maps with numerically reconstructed phase maps using different image quality assessment metrics.
Multispectral quantitative phase imaging (MS-QPI) is a high-contrast label-free technique for morphological imaging of the specimens. The aim of the present study is to extract spectral dependent quantitative information in single-shot using a highly spatially sensitive digital holographic microscope assisted by a deep neural network. There are three different wavelengths used in our method: lambda = 532, 633, and 808 nm. The first step is to get the interferometric data for each wavelength. The acquired datasets are used to train a generative adversarial network to generate multispectral (MS) quantitative phase maps from a single input interferogram. The network was trained and validated on two different samples: the optical waveguide and MG63 osteosarcoma cells. Validation of the present approach is performed by comparing the predicted MS phase maps with numerically reconstructed (FT + TIE) phase maps and quantifying with different image quality assessment metrices. (c) 2023 Optica Publishing Group
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