L
Lei Tian
Researcher at Boston University
Publications - 229
Citations - 6696
Lei Tian is an academic researcher from Boston University. The author has contributed to research in topics: Phase retrieval & Computer science. The author has an hindex of 34, co-authored 192 publications receiving 5027 citations. Previous affiliations of Lei Tian include Massachusetts Institute of Technology & University of California, Berkeley.
Papers
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Multiplexed coded illumination for Fourier Ptychography with an LED array microscope.
TL;DR: A multiplexed illumination strategy in which multiple randomly selected LEDs are turned on for each image so that the total number of images can be significantly reduced, without sacrificing image quality.
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3D intensity and phase imaging from light field measurements in an LED array microscope
Lei Tian,Laura Waller +1 more
TL;DR: In this article, the Fourier ptychography was used to estimate the 3D complex transmittance function of the sample at multiple depths, without any weak or single-scattering approximations.
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Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media
Yunzhe Li,Yujia Xue,Lei Tian +2 more
TL;DR: In this article, the authors proposed a statistical "one-to-all" deep learning (DL) technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations.
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Transport of Intensity phase-amplitude imaging with higher order intensity derivatives
TL;DR: A method for improving the accuracy of phase retrieval based on the Transport of Intensity equation is demonstrated by using intensity measurements at multiple planes to estimate and remove the artifacts due to higher order axial derivatives.
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Experimental robustness of Fourier ptychography phase retrieval algorithms
Li-Hao Yeh,Jonathan Dong,Jingshan Zhong,Lei Tian,Michael Chen,Gongguo Tang,Mahdi Soltanolkotabi,Laura Waller +7 more
TL;DR: In this article, the authors compare and classify multiple Fourier ptychography inverse algorithms in terms of experimental robustness and find that the main sources of error are noise, aberrations and mis-calibration (i.e. model mis-match).