<ul data-eligibleForWebStory="true"><li>Researchers have developed a neuromorphic optical engineering and computational approach to track and image moving targets obscured by scattering media.</li><li>The method combines an event detecting camera with multistage neuromorphic deep learning for object localization and identification.</li><li>Photon signals from scattering media are converted to pixel-wise asynchronized spike trains by the event camera to filter out background noise.</li><li>A deep spiking neural network (SNN) processes the spiking data for simultaneous tracking and image reconstruction of objects.</li><li>The approach successfully tracked and imaged randomly moving objects in dense turbid media and dynamic stationary objects.</li><li>Standardized character sets were used to represent complex objects, showcasing the method's versatility.</li><li>The study emphasizes the benefits of a fully neuromorphic approach in achieving efficient imaging technology with low power consumption.</li></ul>

Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets through Strongly Scattering Media

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