Single shot real-time high-resolution imaging through dynamic turbid media based on deep learning

Abstract 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.

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