A deep learning approach for reconstruction in temporal compressed imaging

Linxia Zhang; Jun Ke; Edmund Y. Lam

A deep learning approach for reconstruction in temporal compressed imaging

Linxia Zhang
, Jun Ke^*
, Edmund Y. Lam

^*Corresponding author for this work

School of Optics and Photonics

Research output: Contribution to journal › Conference article › peer-review

Abstract

We discuss a deep network for temporal compressed imaging. The data collected by an experimental platform are used for training and testing. Spatial variant point spread functions are calibrated for reconstruction.

Original language	English
Article number	CW4B.3
Journal	Optics InfoBase Conference Papers
Publication status	Published - 2020
Event	Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020 - Virtual, Online, United States Duration: 22 Jun 2020 → 26 Jun 2020

Cite this

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title = "A deep learning approach for reconstruction in temporal compressed imaging",

abstract = "We discuss a deep network for temporal compressed imaging. The data collected by an experimental platform are used for training and testing. Spatial variant point spread functions are calibrated for reconstruction.",

author = "Linxia Zhang and Jun Ke and Lam, \{Edmund Y.\}",

note = "Publisher Copyright: {\textcopyright} 2020 The Author(s); Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020 ; Conference date: 22-06-2020 Through 26-06-2020",

year = "2020",

language = "English",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

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TY - JOUR

T1 - A deep learning approach for reconstruction in temporal compressed imaging

AU - Zhang, Linxia

AU - Ke, Jun

AU - Lam, Edmund Y.

PY - 2020

Y1 - 2020

N2 - We discuss a deep network for temporal compressed imaging. The data collected by an experimental platform are used for training and testing. Spatial variant point spread functions are calibrated for reconstruction.

AB - We discuss a deep network for temporal compressed imaging. The data collected by an experimental platform are used for training and testing. Spatial variant point spread functions are calibrated for reconstruction.

UR - https://www.scopus.com/pages/publications/85118637136

M3 - Conference article

AN - SCOPUS:85118637136

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - CW4B.3

T2 - Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020

Y2 - 22 June 2020 through 26 June 2020

ER -

A deep learning approach for reconstruction in temporal compressed imaging

Abstract

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