Ptychography-based high-throughput lensless on-chip microscopy via incremental proximal algorithms

Abstract

Ptychography-based lensless on-chip microscopy enables high-throughput imaging by retrieving the missing phase information from intensity measurements. Numerous reconstruction algorithms for ptychography have been proposed, yet only a few incremental algorithms can be extended to lensless on-chip microscopy because of large-scale datasets but limited computational efficiency. In this paper, we propose the use of accelerated proximal gradient methods for blind ptychographic phase retrieval in lensless on-chip microscopy. Incremental gradient approaches are adopted in the reconstruction routine. Our algorithms divide the phase retrieval problem into sub-problems involving the evaluation of proximal operator, stochastic gradient descent, and Wirtinger derivatives. We benchmark the performances of accelerated proximal gradient, extended ptychographic iterative engine, and alternating direction method of multipliers, and discuss their convergence and accuracy in both noisy and noiseless cases. We also validate our algorithms using experimental datasets, where full field of view measurements are captured to recover the high-resolution complex samples. Among these algorithms, accelerated proximal gradient presents the overall best performance regarding accuracy and convergence rate. The proposed methods may find applications in ptychographic reconstruction, especially for cases where a wide field of view and high resolution are desired at the same time.