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

^*此作品的通讯作者

光电学院

科研成果: 期刊稿件 › 会议文章 › 同行评审

摘要

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.

源语言	英语
文章编号	CW4B.3
期刊	Optics InfoBase Conference Papers
出版状态	已出版 - 2020
活动	Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020 - Virtual, Online, 美国期限: 22 6月 2020 → 26 6月 2020

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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.}",

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year = "2020",

language = "English",

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AU - Ke, Jun

AU - Lam, Edmund Y.

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AN - SCOPUS:85118637136

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JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

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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

摘要

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