Low signal-to-noise ratio (SNR) measurement is conceivable the primary obstruction to real-time, high-resolution through dynamic turbid media optical imaging. To break this restriction, by individualizing and employing these low SNR measurement data, the spectrum estimation theory is procured a noise model for scatter imaging. The noise model proposed is exploited to synthesize data set training to settle the related problems of noise phase without knowing the experimental scenes. We verify the robustness of the resulting deep correlography method to noise, outdistance the capabilities of the existing Fourier-domain shower-curtain effect (FDSE) system in terms of spatial resolution and total acquisition time, in addition, the targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure.

低信噪比 (SNR) 测量可能是通过动态混浊介质光学成像实现实时、高分辨率的主要障碍。为了打破这种限制，通过对这些低信噪比测量数据进行个体化和利用，谱估计理论获得了散射成像的噪声模型。利用所提出的噪声模型来合成数据集训练，以解决不了解实验场景的噪声相位相关问题。我们验证了由此产生的深度相关成像方法对噪声的鲁棒性，在空间分辨率和总采集时间方面超过了现有傅立叶域淋浴帘效应 (FDSE) 系统的能力，此外，目标可以从标准 sCMOS 探测器，曝光时间为 150 毫秒。