Numerical Spectral Demulitplexing Microscopy of Measurements from an Anatomical Specimen

Jason Deglint, Farnoud Kazemzadeh, Alexander Wong, David Clausi


Multispectral microscopy is a method of capturing spectral bands
using a microscope, and is used to observe specimens on a micron
or nano scale. However, these systems are limited because they
cannot capture transient phenomena since they cannot capture simultaneous
spectral information. We propose a new method called
numerical spectral demultiplexing microscopy (NSDM) which utilizes
a Raspberry Pi camera to capture RGB measurements and
then infer narrow-band multispectral spectra. This is accomplished
by training a non-linear regression random forest model based on
the spectral sensitivity of the camera which allows for a low-cost,
portable, and simultaneous capture multispectral microscopy system.
We use the NSDM system as a bright-field multispectral microscope
and a dark-field fluorescence multispectral microscope
on an anatomical specimen and show that additional information
can be gathered by combining a bright-field and dark-field fluorescence

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