Imaging through nonstatic scattering media is one of the major challenges in optics, and encountered in imaging through dense fog, turbid water, and many other situations. Here, we propose a method to achieve single-shot incoherent imaging through highly nonstatic and optically thick turbid media by using an end-to-end deep neural network. In this study, we use fat emulsion suspensions in a glass tank as a turbid medium and an additional incoherent light to introduce strong interference noise. We calibrate that the optical thickness of the tank of turbid media is as high as 16, and the signal-to-interference ratio is as low as $-17\mathrm{dB}$. Experimental results show that the proposed learning-based approach can reconstruct the object image with high fidelity in this severe environment.

中文翻译：

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基于神经网络的高度非静态且光学厚的浑浊介质的非相干成像

通过非静态散射介质成像是光学的主要挑战之一，并且在通过浓雾，浑浊的水和许多其他情况进行成像时遇到了挑战。在这里，我们提出了一种通过使用端到端的深度神经网络通过高度非静态和光学上较厚的混浊介质来实现单次非相干成像的方法。在这项研究中，我们将玻璃罐中的脂肪乳状液悬浮液用作混浊介质，并使用附加的非相干光引入强干扰噪声。我们校准了混浊介质罐的光学厚度高达16，信噪比低至$-17\mathrm{D\; b}$。实验结果表明，所提出的基于学习的方法可以在这种恶劣的环境下以高保真度重建物体图像。