Imperial College London
Alternate

DrAmandaFoust

Faculty of Engineering, Department of Bioengineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 1055a.foust Website CV

 
 
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Location

 

RSM 4.05Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Verinaz-Jadan:2022:10.1109/TCI.2022.3160667,
author = {Verinaz-Jadan, H and Song, P and Howe, CL and Foust, AJ and Dragotti, PL},
doi = {10.1109/TCI.2022.3160667},
journal = {IEEE Transactions on Computational Imaging},
pages = {286--301},
title = {Shift-invariant-subspace discretization and volume reconstruction for light field microscopy},
url = {http://dx.doi.org/10.1109/TCI.2022.3160667},
volume = {8},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Light Field Microscopy (LFM) is an imaging technique that captures 3D spatial information with a single 2D image. LFM is attractive because of its relatively simple implementation and fast volume acquisition rate. Capturing volume time series at a camera frame rate can enable the study of the behaviour of many biological systems. For instance, it could provide insights into the communication dynamics of living 3D neural networks. However, conventional 3D reconstruction algorithms for LFM typically suffer from high computational cost, low lateral resolution, and reconstruction artifacts. In this work, we study the origin of these issues and propose novel techniques to improve the performance of the reconstruction process. First, we propose a discretization approach that uses shift-invariant subspaces to generalize the typical discretization framework used in LFM. Then, we study the shift-invariant-subspace assumption as a prior for volume reconstruction under ideal conditions. Furthermore, we present a method to reduce the computational time of the forward model by using singular value decomposition (SVD). Finally, we propose to use iterative approaches that incorporate additional priors to perform artifact-free 3D reconstruction from real light field images. We experimentally show that our approach performs better than Richardson-Lucy-based strategies in computational time, image quality, and artifact reduction.
AU  - Verinaz-Jadan,H
AU  - Song,P
AU  - Howe,CL
AU  - Foust,AJ
AU  - Dragotti,PL
DO  - 10.1109/TCI.2022.3160667
EP  - 301
PY  - 2022///
SN  - 2573-0436
SP  - 286
TI  - Shift-invariant-subspace discretization and volume reconstruction for light field microscopy
T2  - IEEE Transactions on Computational Imaging
UR  - http://dx.doi.org/10.1109/TCI.2022.3160667
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000782797100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/9738467
UR  - http://hdl.handle.net/10044/1/97595
VL  - 8
ER  -
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