Dataset for: Characterizing optical fiber transmission matrices using metasurface reflector stacks for lensless imaging without distal access
Repository URI
Repository DOI
Change log
Authors
Gordon, George SD
Ramos, Alberto Gil CP
Williams, Calum
Description
Code to reconstruction transmission matrices from reflection matrices, as well as some sample matrices used to produce Figure 8 of the paper.
Version
Software / Usage instructions
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
Publisher
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)
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)