  Mask-based lensless imagers are smaller and lighter than traditional lensed
cameras. In these imagers, the sensor does not directly record an image of the
scene; rather, a computational algorithm reconstructs it. Typically, mask-based
lensless imagers use a model-based reconstruction approach that suffers from
long compute times and a heavy reliance on both system calibration and
heuristically chosen denoisers. In this work, we address these limitations
using a bounded-compute, trainable neural network to reconstruct the image. We
leverage our knowledge of the physical system by unrolling a traditional
model-based optimization algorithm, whose parameters we optimize using
experimentally gathered ground-truth data. Optionally, images produced by the
unrolled network are then fed into a jointly-trained denoiser. As compared to
traditional methods, our architecture achieves better perceptual image quality
and runs 20x faster, enabling interactive previewing of the scene. We explore a
spectrum between model-based and deep learning methods, showing the benefits of
using an intermediate approach. Finally, we test our network on images taken in
the wild with a prototype mask-based camera, demonstrating that our network
generalizes to natural images.
