Displacement-agnostic coherent imaging through scatter with an interpretable deep neural network

PLUTO / PLUTO-2 Spatial Light Modulators
Deep Learning / Neuronal Network Misc.: Speckle / Characterization / Metrology Turbid-/ Opaque Media /Multi Scattering
Published on:
Authors: Li, Yunzhe; Cheng, Shiyi; Xue, Yujia & Tian, Lei
Abstract:

“Coherent imaging through scatter is a challenging task. Both model-based and data-driven approaches have been explored to solve the inverse scattering problem. In our previous work, we have shown that a deep learning approach can make high-quality and highly generalizable predictions through unseen diffusers. Here, we propose a new deep neural network model that is agnostic to a broader class of perturbations including scatterer change, displacements, and system defocus up to 10× depth of field. In addition, we develop a new analysis framework for interpreting the mechanism of our deep learning model and visualizing its generalizability based on an unsupervised dimension reduction technique. We show that our model can unmix the scattering-specific information and extract the object-specific information and achieve generalization under different scattering conditions. Our work paves the way to a robust and interpretable deep learning approach to imaging through scattering media.”

Open Access

Publication: Optics Express
Issue/Year: Optics Express, Volume 29; Number 2; Pages 2244; 2021
DOI: 10.1364/oe.411291
Link: https://doi.org/10.1364/oe.411291

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