Event-Driven Imaging in Turbid Media: A Confluence of Optoelectronics and Neuromorphic Computation

TL;DR

This paper introduces a novel optical-computational method inspired by human vision, combining neuromorphic computing and event-driven imaging to reconstruct images obscured by turbid media, outperforming traditional imaging in challenging conditions.

Contribution

It presents the first integration of neuromorphic computation with event-driven imaging for imaging through turbid media, demonstrating effective reconstruction of obscured images.

Findings

Successful reconstruction of MNIST characters through turbid media

Neuromorphic approach outperforms traditional imaging in obscured conditions

Method applicable to dynamic and stationary targets in scattering media

Abstract

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.