  Computational imaging through scatter generally is accomplished by first
characterizing the scattering medium so that its forward operator is obtained;
and then imposing additional priors in the form of regularizers on the
reconstruction functional so as to improve the condition of the originally
ill-posed inverse problem. In the functional, the forward operator and
regularizer must be entered explicitly or parametrically (e.g. scattering
matrices and dictionaries, respectively.) However, the process of determining
these representations is often incomplete, prone to errors, or infeasible.
Recently, deep learning architectures have been proposed to instead learn both
the forward operator and regularizer through examples. Here, we propose for the
first time, to our knowledge, a convolutional neural network architecture
called "IDiffNet" for the problem of imaging through diffuse media and
demonstrate that IDiffNet has superior generalization capability through
extensive tests with well-calibrated diffusers. We found that the Negative
Pearson Correlation Coefficient loss function for training is more appropriate
for spatially sparse objects and strong scattering conditions. Our results show
that the convolutional architecture is robust to the choice of prior, as
demonstrated by the use of multiple training and testing object databases, and
capable of achieving higher space-bandwidth product reconstructions than
previously reported.
