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.

本文介绍了利用边界计算的可训练神经网络结构，基于物理系统的先验知识优化传统基于模型的重建算法参数，并通过实验验证其在实际图像中的推广性和效果优越性，从而在掩膜无透镜成像器中实现更好的感知图像质量和更快速的图像采集处理。