Incoherent imaging through highly nonstatic and optically thick turbid media based on neural network
Shanshan Zheng, Hao Wang, Dong Shi, Fei Wang, Guohai Situ
Abstract
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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mo form="prefix">−</mml:mo> <mml:mn>17</mml:mn> <mml:mtext> </mml:mtext> <mml:mi>dB</mml:mi> </mml:mrow> </mml:math> . Experimental results show that the proposed learning-based approach can reconstruct the object image with high fidelity in this severe environment.