  Imaging through scattering is an important, yet challenging problem.
Tremendous progress has been made by exploiting the deterministic input-output
"transmission matrix" for a fixed medium. However, this "one-to-one" mapping is
highly susceptible to speckle decorrelations - small perturbations to the
scattering medium lead to model errors and severe degradation of the imaging
performance. Our goal here is to develop a new framework that is highly
scalable to both medium perturbations and measurement requirement. To do so, we
propose a statistical "one-to-all" deep learning technique that encapsulates a
wide range of statistical variations for the model to be resilient to speckle
decorrelations. Specifically, we develop a convolutional neural network (CNN)
that is able to learn the statistical information contained in the speckle
intensity patterns captured on a set of diffusers having the same macroscopic
parameter. We then show for the first time, to the best of our knowledge, that
the trained CNN is able to generalize and make high-quality object predictions
through an entirely different set of diffusers of the same class. Our work
paves the way to a highly scalable deep learning approach for imaging through
scattering media.
