High-throughput computational imaging requires efficient processing
algorithms to retrieve multi-dimensional and multi-scale information. In
computational phase imaging, phase retrieval (PR) is required to reconstruct
both amplitude and phase in complex space from intensity-only measurements. The
existing PR algorithms suffer from the tradeoff among low computational
complexity, robustness to measurement noise and strong generalization on
different modalities. In this work, we report an efficient large-scale phase
retrieval technique termed as LPR. It extends the plug-and-play
generalized-alternating-projection framework from real space to nonlinear
complex space. The alternating projection solver and enhancing neural network
are respectively derived to tackle the measurement formation and statistical
prior regularization. This framework compensates the shortcomings of each
operator, so as to realize high-fidelity phase retrieval with low computational
complexity and strong generalization. We applied the technique for a series of
computational phase imaging modalities including coherent diffraction imaging,
coded diffraction pattern imaging, and Fourier ptychographic microscopy.
Extensive simulations and experiments validate that the technique outperforms
the existing PR algorithms with as much as 17dB enhancement on signal-to-noise
ratio, and more than one order-of-magnitude increased running efficiency.
Besides, we for the first time demonstrate ultra-large-scale phase retrieval at
the 8K level (7680$\times$4320 pixels) in minute-level time.

本文介绍了一种高效且具有强大泛化能力的大规模相位恢复技术，其使用交替投影算法和增强的神经网络分别处理测量和统计优化问题，有效地弥补了各个操作符的缺点，并在计算成本较低的前提下实现了大规模相位恢复，被应用于计算相位成像中的各种模态并验证了其优越性。