  Critical aspects of computational imaging systems, such as experimental
design and image priors, can be optimized through deep networks formed by the
unrolled iterations of classical model-based reconstructions (termed
physics-based networks). However, for real-world large-scale inverse problems,
computing gradients via backpropagation is infeasible due to memory limitations
of graphics 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 systems. We
demonstrate our method on a small-scale compressed sensing example, as well as
two large-scale real-world systems: multi-channel magnetic resonance imaging
and super-resolution optical microscopy.
