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：我们提出了一种用于大规模高动态范围计算成像的新方法。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，但在深度神经网络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——其中去噪深度神经网络对测量设置不敏感——已被证明能有效应对可扩展性和高动态范围挑战，但严重依赖高度迭代的算法。我们提出了一种残差深度神经网络系列方法，也可被解释为匹配追踪的学习版本。在该方法中，重建图像是逐步提升动态范围的残差图像之和，并由深度神经网络迭代估计，每步输入为前一步的反投影数据残差。我们在射电天文成像模拟中证明：仅需少量项数的系列，就能以极低的计算成本达到与即插即用方法相竞争的重建质量。