  Coherent imaging through scatter is a challenging task in computational
imaging. 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 (DNN) model that is agnostic to a broader class of perturbations
including scatterer change, displacements, and system defocus up to 10X depth
of field. In addition, we develop a new analysis framework for interpreting the
mechanism of our DNN model and visualizing its generalizability based on an
unsupervised dimension reduction technique. We show that our DNN can unmix the
scattering-specific information and extract the object-specific information so
as to achieve generalization under different scattering conditions. Our work
paves the way to a highly robust and interpretable deep learning approach to
imaging through scattering media.
