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Titlebook: Computational Optical Phase Imaging; Cheng Liu,Shouyu Wang,Suhas P. Veetil Book 2022 The Editor(s) (if applicable) and The Author(s), unde

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书目名称Computational Optical Phase Imaging
编辑Cheng Liu,Shouyu Wang,Suhas P. Veetil
视频video
概述Provides an overview of‘recent advances in computational optical phase imaging‘techniques.Includes several‘MATLAB® simulations along with codes.Covers modern applications of computational optical phas
丛书名称Progress in Optical Science and Photonics
图书封面Titlebook: Computational Optical Phase Imaging;  Cheng Liu,Shouyu Wang,Suhas P. Veetil Book 2022 The Editor(s) (if applicable) and The Author(s), unde
描述.In this book, computational optical phase imaging techniques are presented along with Matlab codes that allow the reader to run their own simulations and gain a thorough understanding of the current state-of-the-art.  The book focuses on modern applications of computational optical phase imaging in engineering measurements and biomedical imaging. Additionally, it discusses the future of computational optical phase imaging, especially in terms of system miniaturization and deep learning-based phase retrieval..
出版日期Book 2022
关键词Optical imaging; Scattered light; Phase information; Image reconstruction; Coherent Diffraction Imaging
版次1
doihttps://doi.org/10.1007/978-981-19-1641-0
isbn_softcover978-981-19-1643-4
isbn_ebook978-981-19-1641-0Series ISSN 2363-5096 Series E-ISSN 2363-510X
issn_series 2363-5096
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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Kreative Materialmedien in der SupervisionIn recent years, phase imaging has experienced tremendous success and has opened up new exciting frontiers. As an example, deep learning algorithms are capable of learning patterns and rules from a series of data sets in order to perform specific tasks.
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Recent Trends in Computational Optical Phase Imaging,In recent years, phase imaging has experienced tremendous success and has opened up new exciting frontiers. As an example, deep learning algorithms are capable of learning patterns and rules from a series of data sets in order to perform specific tasks.
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Interference-Based Quantitative Optical Phase Imaging,st microscopy has helped scientists uncover the structure and function of cells and subcellular organelles by generating image contrast from transparent samples with insufficient intensity variations to generate a brightness contrast image.
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