Sampling for Snapshot Compressive Imaging
Publication
, Journal Article
Hu, M; Wu, Z; Huang, Q; Yuan, X; Brady, D
Published in: Intelligent Computing
January 1, 2023
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.
Duke Scholars
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Published In
Intelligent Computing
DOI
EISSN
2771-5892
Publication Date
January 1, 2023
Volume
2
Citation
APA
Chicago
ICMJE
MLA
NLM
Hu, M., Wu, Z., Huang, Q., Yuan, X., & Brady, D. (2023). Sampling for Snapshot Compressive Imaging. Intelligent Computing, 2. https://doi.org/10.34133/icomputing.0038
Hu, M., Z. Wu, Q. Huang, X. Yuan, and D. Brady. “Sampling for Snapshot Compressive Imaging.” Intelligent Computing 2 (January 1, 2023). https://doi.org/10.34133/icomputing.0038.
Hu M, Wu Z, Huang Q, Yuan X, Brady D. Sampling for Snapshot Compressive Imaging. Intelligent Computing. 2023 Jan 1;2.
Hu, M., et al. “Sampling for Snapshot Compressive Imaging.” Intelligent Computing, vol. 2, Jan. 2023. Scopus, doi:10.34133/icomputing.0038.
Hu M, Wu Z, Huang Q, Yuan X, Brady D. Sampling for Snapshot Compressive Imaging. Intelligent Computing. 2023 Jan 1;2.
Published In
Intelligent Computing
DOI
EISSN
2771-5892
Publication Date
January 1, 2023
Volume
2