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Shift-invariant-subspace discretization and volume reconstruction for light field microscopy

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Title: Shift-invariant-subspace discretization and volume reconstruction for light field microscopy
Authors: Verinaz-Jadan, H
Song, P
Howe, CL
Foust, AJ
Dragotti, PL
Item Type: Journal Article
Abstract: 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.
Issue Date: 18-Mar-2022
Date of Acceptance: 1-Mar-2022
URI: http://hdl.handle.net/10044/1/97595
DOI: 10.1109/TCI.2022.3160667
ISSN: 2573-0436
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 286
End Page: 301
Journal / Book Title: IEEE Transactions on Computational Imaging
Volume: 8
Copyright Statement: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Sponsor/Funder: Biotechnology and Biological Sciences Research Council (BBSRC)
Funder's Grant Number: BB/R009007/1
Keywords: Science & Technology
Technology
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Engineering
Artifact-free deconvolution
light field microscopy
shift-invariant subspaces
system discretization
volume reconstruction
DECONVOLUTION
Science & Technology
Technology
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Engineering
Artifact-free deconvolution
light field microscopy
shift-invariant subspaces
system discretization
volume reconstruction
DECONVOLUTION
Publication Status: Published
Appears in Collections:Bioengineering
Electrical and Electronic Engineering
Faculty of Engineering