  Computational imaging systems jointly design computation and hardware to
retrieve information which is not traditionally accessible with standard
imaging systems. Recently, critical aspects such as experimental design and
image priors are optimized through deep neural networks formed by the unrolled
iterations of classical physics-based reconstructions (termed physics-based
networks). However, for real-world large-scale systems, computing gradients via
backpropagation restricts learning due to memory limitations of graphical
processing units. In this work, we propose a memory-efficient learning
procedure that exploits the reversibility of the network's layers to enable
data-driven design for large-scale computational imaging. We demonstrate our
methods practicality on two large-scale systems: super-resolution optical
microscopy and multi-channel magnetic resonance imaging.
