Event-Driven Imaging in Turbid Media: A Confluence of Optoelectronics
  and Neuromorphic Computation
Ning Zhang Timothy Shea Arto Nurmikko
In this paper a new optical-computational method is introduced to unveil
images of targets whose visibility is severely obscured by light scattering in
dense, turbid media. The targets of interest are taken to be dynamic in that
their optical properties are time-varying whether stationary in space or
moving. The scheme, to our knowledge the first of its kind, is human vision
inspired whereby diffuse photons collected from the turbid medium are first
transformed to spike trains by a dynamic vision sensor as in the retina, and
image reconstruction is then performed by a neuromorphic computing approach
mimicking the brain. We combine benchtop experimental data in both reflection
(backscattering) and transmission geometries with support from physics-based
simulations to develop a neuromorphic computational model and then apply this
for image reconstruction of different MNIST characters and image sets by a
dedicated deep spiking neural network algorithm. Image reconstruction is
achieved under conditions of turbidity where an original image is
unintelligible to the human eye or a digital video camera, yet clearly and
quantifiable identifiable when using the new neuromorphic computational
approach.