Dataset for: Characterizing optical fiber transmission matrices using metasurface reflector stacks for lensless imaging without distal access
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Authors
Ramos, Alberto Gil CP
Williams, Calum
Yoon, Jonghee
Publication Date
2020-05-18Type
Dataset
Metadata
Show full item recordCitation
Gordon, G., Gataric, M., Ramos, A. G. C., Mouthaan, R., Williams, C., Yoon, J., Wilkinson, T., & et al. (2020). Dataset for: Characterizing optical fiber transmission matrices using
metasurface reflector stacks for lensless imaging without distal access [Dataset]. https://doi.org/10.17863/CAM.45121
Description
Code to reconstruction transmission matrices from reflection matrices, as well as some sample matrices used to produce Figure 8 of the paper.
Format
Instructions:
Run the file localoptimisation.py using Python. You will need to have Tensorflow installed (I recommend the GPU version). Run the code once until it starts to oscilliate then run it again (starting from the previous starting point) with a different optimiser, e.g. basic Stochastic Gradient instead of ADAM, or with a lower learning rate. That often helps to get the recovered matrix very close to the ideal.
The code first generations reflection matrices and then uses the known reflectors and wavelengths to recover the original transmission matrices.
Files:
currentGraph_sample.png shows the type of response graph you might expect if starting from a random starting point.
reflectors.mat give the reflector matrices used
simSettings.mat gives the wavlengths etc. used in the simulation
target.mat gives the multi-wavelength matrices you are trying to recover
Keywords
optics, optical fibres, imaging, endoscopy, metasurfaces, nanostructures, polarisation
Relationships
Publication Reference: https://doi.org/10.1103/PhysRevX.9.041050https://www.repository.cam.ac.uk/handle/1810/301540
Sponsorship
European Commission (630729)
Engineering and Physical Sciences Research Council (EP/N014588/1)
Cancer Research UK (C14303/A17197)
Cancer Research UK (21102)
Cancer Research UK (24669)
Engineering and Physical Sciences Research Council (EP/R003599/1)
Identifiers
This record's DOI: https://doi.org/10.17863/CAM.45121
Rights
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/
Statistics
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IRUS guide.