  Deep learning has been proven to yield reliably generalizable answers to
numerous classification and decision tasks. Here, we demonstrate for the first
time, to our knowledge, that deep neural networks (DNNs) can be trained to
solve inverse problems in computational imaging. We experimentally demonstrate
a lens-less imaging system where a DNN was trained to recover a phase object
given a raw intensity image recorded some distance away.
