We propose a new approach for large-scale high-dynamic range computational imaging. Deep Neural Networks (DNNs) trained end-to-end can solve linear inverse imaging problems almost instantaneously. While unfolded architectures provide robustness to measurement setting variations, embedding large-scale measurement operators in DNN architectures is impractical. Alternative Plug-and-Play (PnP) approaches, where the denoising DNNs are blind to the measurement setting, have proven effective to address scalability and high-dynamic range challenges, but rely on highly iterative algorithms. We propose a residual DNN series approach, also interpretable as a learned version of matching pursuit, where the reconstructed image is a sum of residual images progressively increasing the dynamic range, and estimated iteratively by DNNs taking the back-projected data residual of the previous iteration as input. We demonstrate on radio-astronomical imaging simulations that a series of only few terms provides a reconstruction quality competitive with PnP, at a fraction of the cost.

翻译：我们提出了一种新的大规模高动态范围计算成像方法。经过端到端训练的深度神经网络（DNN）能够几乎即时地解决线性逆成像问题。虽然展开式架构对测量设置的变化具有鲁棒性，但在DNN架构中嵌入大规模测量算子并不实用。替代性的即插即用（PnP）方法中，去噪DNN对测量设置是盲的，已被证明能有效解决可扩展性和高动态范围挑战，但依赖高度迭代的算法。我们提出了一种残差DNN序列方法，也可解释为匹配追踪的学习版本，其中重建图像是逐步增加动态范围的残差图像之和，并由DNN迭代估计，这些DNN将前一次迭代的反投影数据残差作为输入。我们在射电天文成像模拟中证明，仅包含少量项（数项）的序列便能提供与PnP相当的图像重建质量，而计算成本仅为PnP的一小部分。