Imperial College London
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ProfessorPier LuigiDragotti

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Signal Processing
 
 
 
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Contact

 

+44 (0)20 7594 6192p.dragotti

 
 
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Location

 

814Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Song:2020:10.1109/TCI.2020.2997301,
author = {Song, P and Verinaz, Jadan H and Howe, C and Quicke, P and Foust, A and Dragotti, PL},
doi = {10.1109/TCI.2020.2997301},
journal = {IEEE transactions on computational imaging},
pages = {1017--1032},
title = {3D localization for light-field microscopy via convolutional sparse coding on epipolar images},
url = {http://dx.doi.org/10.1109/TCI.2020.2997301},
volume = {6},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Light-field microscopy (LFM) is a type of all-optical imaging system that is able to capture 4D geometric information of light rays and can reconstruct a 3D model from a single snapshot. In this paper, we propose a new 3D localization approach to effectively detect 3D positions of neuronal cells from a single light-field image with high accuracy and outstanding robustness to light scattering. This is achieved by constructing a depth-aware dictionary and by combining it with convolutional sparse coding. Specifically, our approach includes 3 key parts: light-field calibration, depth-aware dictionary construction, and localization based on convolutional sparse coding (CSC). In the first part, an observed raw light-field image is calibrated and then decoded into a two-plane parameterized 4D format which leads to the epi-polar plane image (EPI). The second part involves simulating a set of light-fields using a wave-optics forward model for a ball-shaped volume that is located at different depths. Then, a depth-aware dictionary is constructed where each element is a synthetic EPI associated to a specific depth. Finally, by taking full advantage of the sparsity prior and shift-invariance property of EPI, 3D localization is achieved via convolutional sparse coding on an observed EPI with respect to the depth-aware EPI dictionary. We evaluate our approach on both non-scattering specimen (fluorescent beads suspended in agarose gel) and scattering media (brain tissues of genetically encoded mice). Extensive experiments demonstrate that our approach can reliably detect the 3D positions of granular targets with small Root Mean Square Error (RMSE), high robustness to optical aberration and light scattering in mammalian brain tissues.
AU  - Song,P
AU  - Verinaz,Jadan H
AU  - Howe,C
AU  - Quicke,P
AU  - Foust,A
AU  - Dragotti,PL
DO  - 10.1109/TCI.2020.2997301
EP  - 1032
PY  - 2020///
SN  - 2333-9403
SP  - 1017
TI  - 3D localization for light-field microscopy via convolutional sparse coding on epipolar images
T2  - IEEE transactions on computational imaging
UR  - http://dx.doi.org/10.1109/TCI.2020.2997301
UR  - http://hdl.handle.net/10044/1/80567
VL  - 6
ER  -
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