CMSnet: State of the Art Deep Learning Multiscale Reconstruction for Compressive Sensing

Vladislav Kravets; Adrian Stern

doi:https://doi.org/10.1364/3D.2022.JW5C.3

CMSnet: State of the Art Deep Learning Multiscale Reconstruction for Compressive Sensing

Vladislav Kravets, Adrian Stern

Unit of Photonics and Electro-Optics Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We present what is, to the best of our knowledge, state-of-the-art reconstruction results for deep learning-based multiscale compressive sensing. Our reconstruction method is compared to a variety of recent compressive sensing reconstruction methods.

Original language	English
Title of host publication	Computational Optical Sensing and Imaging, COSI 2022
Publisher	Optica Publishing Group (formerly OSA)
ISBN (Electronic)	9781557528209
DOIs	https://doi.org/10.1364/3D.2022.JW5C.3
State	Published - 1 Jan 2022
Event	Computational Optical Sensing and Imaging, COSI 2022 - Vancouver, Canada Duration: 11 Jul 2022 → 15 Jul 2022

Publication series

Name	Optics InfoBase Conference Papers

Conference

Conference	Computational Optical Sensing and Imaging, COSI 2022
Country/Territory	Canada
City	Vancouver
Period	11/07/22 → 15/07/22

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

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https://doi.org/10.1364/3D.2022.JW5C.3

Cite this

@inproceedings{bfcba1244ed44b1dace26790e279edf1,

title = "CMSnet: State of the Art Deep Learning Multiscale Reconstruction for Compressive Sensing",

abstract = "We present what is, to the best of our knowledge, state-of-the-art reconstruction results for deep learning-based multiscale compressive sensing. Our reconstruction method is compared to a variety of recent compressive sensing reconstruction methods.",

author = "Vladislav Kravets and Adrian Stern",

note = "Publisher Copyright: {\textcopyright} 2022 The Author(s); Computational Optical Sensing and Imaging, COSI 2022 ; Conference date: 11-07-2022 Through 15-07-2022",

year = "2022",

month = jan,

day = "1",

doi = "https://doi.org/10.1364/3D.2022.JW5C.3",

language = "English",

series = "Optics InfoBase Conference Papers",

publisher = "Optica Publishing Group (formerly OSA)",

booktitle = "Computational Optical Sensing and Imaging, COSI 2022",

}

Kravets, V & Stern, A 2022, CMSnet: State of the Art Deep Learning Multiscale Reconstruction for Compressive Sensing. in Computational Optical Sensing and Imaging, COSI 2022. Optics InfoBase Conference Papers, Optica Publishing Group (formerly OSA), Computational Optical Sensing and Imaging, COSI 2022, Vancouver, Canada, 11/07/22. https://doi.org/10.1364/3D.2022.JW5C.3

TY - GEN

T1 - CMSnet

T2 - Computational Optical Sensing and Imaging, COSI 2022

AU - Kravets, Vladislav

AU - Stern, Adrian

PY - 2022/1/1

Y1 - 2022/1/1

N2 - We present what is, to the best of our knowledge, state-of-the-art reconstruction results for deep learning-based multiscale compressive sensing. Our reconstruction method is compared to a variety of recent compressive sensing reconstruction methods.

AB - We present what is, to the best of our knowledge, state-of-the-art reconstruction results for deep learning-based multiscale compressive sensing. Our reconstruction method is compared to a variety of recent compressive sensing reconstruction methods.

UR - http://www.scopus.com/inward/record.url?scp=85139099006&partnerID=8YFLogxK

U2 - https://doi.org/10.1364/3D.2022.JW5C.3

DO - https://doi.org/10.1364/3D.2022.JW5C.3

M3 - Conference contribution

AN - SCOPUS:85139099006

T3 - Optics InfoBase Conference Papers

BT - Computational Optical Sensing and Imaging, COSI 2022

PB - Optica Publishing Group (formerly OSA)

Y2 - 11 July 2022 through 15 July 2022

ER -

CMSnet: State of the Art Deep Learning Multiscale Reconstruction for Compressive Sensing

Abstract

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Conference

ASJC Scopus subject areas

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