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.

翻译：本文提出一种新的光计算联合方法，用于恢复在稠密混浊介质中因光散射而严重遮挡的目标图像。目标被假定为动态的，即其光学特性随时间变化（无论是空间静止还是移动）。据我们所知，该方案属首创，受人类视觉启发：首先，从混浊介质收集的漫射光子由动态视觉传感器（类似视网膜）转换为脉冲序列；随后，通过模仿大脑的神经形态计算方法进行图像重建。我们结合反射（后向散射）与透射几何结构的实验数据（辅以物理仿真支持），开发了一种神经形态计算模型，并利用专用深度脉冲神经网络算法对MNIST字符及不同图像集进行图像重建。在混浊条件下，原始图像对人类肉眼或数码摄像机而言难以辨识，而采用新的神经形态计算方法则可实现清晰且可量化的识别。