Joint Optimization of Scanning Strategy and Initialization for Accelerated Lensless Coded Ptychography

Abstract

Lensless coded ptychography (CP) acquires multiple intensity measurements by translating either the object or the coded sensor, and reconstructs high-resolution, large field-of-view images via iterative phase retrieval. However, conventional CP suffers from slow reconstruction due to two limitations. First, periodic uniform scanning requires dense measurements to maintain sufficient measurement diversity, resulting in a large number of raw measurements and a high computational burden for iterative phase retrieval. Second, random initialization leads to slow convergence and increases the risk of stagnation in local minima. To address these limitations, we propose a joint optimization of the sampling strategy and initialization for accelerated reconstruction. Specifically, a continuous non-uniform scanning strategy preserves measurement diversity while reducing the number of required measurements. In addition, a low-resolution regularized ptychographic iterative engine (rPIE)-based initialization provides a more accurate starting point and accelerates the convergence of iterative phase retrieval. Both simulations and experiments demonstrate that the proposed approach achieves approximately three-fold faster convergence while maintaining high reconstruction fidelity. The proposed approach offers an effective solution for high-throughput imaging applications, including digital pathology and label-free quantitative phase imaging.