Sampling for Snapshot Compressive Imaging

Minghao Hu; Zongliang Wu; Qian Huang; Xin Yuan; David Brady

doi:10.34133/icomputing.0038

Sampling for Snapshot Compressive Imaging

Minghao Hu, Zongliang Wu, Qian Huang, Xin Yuan, David Brady

Optical Sciences

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

In this study, we compare interlaced and multiscale sampling of smooth manifolds for snapshot compressive imaging. With a particular focus on spectral, spatial, and temporal focal photographic imaging systems, we show that structured transformer networks enable the efficient integration of multiscale manifolds. In the applications considered here, transformer networks enable simpler and more target-specific sampling strategies for compressive tomography.

Original language	English (US)
Article number	0038
Journal	Intelligent Computing
Volume	2
DOIs	https://doi.org/10.34133/icomputing.0038
State	Published - 2023

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Electrical and Electronic Engineering
Human-Computer Interaction
Artificial Intelligence
Computer Science Applications

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10.34133/icomputing.0038

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title = "Sampling for Snapshot Compressive Imaging",

abstract = "In this study, we compare interlaced and multiscale sampling of smooth manifolds for snapshot compressive imaging. With a particular focus on spectral, spatial, and temporal focal photographic imaging systems, we show that structured transformer networks enable the efficient integration of multiscale manifolds. In the applications considered here, transformer networks enable simpler and more target-specific sampling strategies for compressive tomography.",

author = "Minghao Hu and Zongliang Wu and Qian Huang and Xin Yuan and David Brady",

note = "Publisher Copyright: {\textcopyright} 2023 Minghao Hu et al.",

year = "2023",

doi = "10.34133/icomputing.0038",

language = "English (US)",

volume = "2",

journal = "Intelligent Computing",

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AU - Hu, Minghao

AU - Wu, Zongliang

AU - Huang, Qian

AU - Yuan, Xin

AU - Brady, David

PY - 2023

Y1 - 2023

N2 - In this study, we compare interlaced and multiscale sampling of smooth manifolds for snapshot compressive imaging. With a particular focus on spectral, spatial, and temporal focal photographic imaging systems, we show that structured transformer networks enable the efficient integration of multiscale manifolds. In the applications considered here, transformer networks enable simpler and more target-specific sampling strategies for compressive tomography.

AB - In this study, we compare interlaced and multiscale sampling of smooth manifolds for snapshot compressive imaging. With a particular focus on spectral, spatial, and temporal focal photographic imaging systems, we show that structured transformer networks enable the efficient integration of multiscale manifolds. In the applications considered here, transformer networks enable simpler and more target-specific sampling strategies for compressive tomography.

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DO - 10.34133/icomputing.0038

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SN - 2771-5892

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JO - Intelligent Computing

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Sampling for Snapshot Compressive Imaging

Abstract

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