Coded ptychography has emerged as a powerful technique for high-throughput, high-resolution lensless imaging. However, the trade-off between acquisition speed and image quality remains a significant challenge. To address this, we introduce a novel sparsity-regularized approach to coded ptychography that dramatically reduces the number of required measurements while maintaining high reconstruction quality. The reported approach, termed the ptychographic proximal total-variation (PPTV) solver, formulates the reconstruction task as a total variation regularized optimization problem. Unlike previous implementations that rely on specialized hardware or illumination schemes, PPTV integrates seamlessly into existing coded ptychography setups. Through comprehensive numerical simulations, we demonstrate that PPTV-driven coded ptychography can produce accurate reconstructions with as few as eight intensity measurements, a significant reduction compared to conventional methods. Convergence analysis confirms the robustness and stability of the PPTV algorithm. Experimental results from our optical prototype, featuring a disorder-engineered surface for wavefront modulation, validate PPTV's ability to achieve high-throughput, high-resolution imaging with a substantially reduced measurement burden. By enabling high-quality reconstructions from fewer measurements, PPTV paves the way for more compact, efficient, and cost-effective lensless microscopy systems on a chip, with potential applications in digital pathology, endoscopy, point-of-care diagnostics, and high-content screening.

翻译：编码叠层衍射成像已成为高通量、高分辨率无透镜成像的重要技术。然而，采集速度与图像质量之间的权衡仍是关键挑战。为此，我们提出一种新颖的稀疏正则化编码叠层衍射成像方法，在保持高重建质量的同时显著减少所需测量次数。该报告方法称为叠层衍射近端全变分求解器，将重建任务构建为全变分正则化优化问题。与以往依赖专用硬件或照明方案的实现方式不同，PPTV可无缝集成至现有编码叠层衍射成像系统。通过综合数值模拟，我们证明PPTV驱动的编码叠层衍射成像仅需八次强度测量即可获得精确重建，较传统方法显著减少测量次数。收敛性分析证实了PPTV算法的鲁棒性与稳定性。基于波前调制无序工程表面构建的光学原型实验结果表明，PPTV能以大幅降低的测量负担实现高通量、高分辨率成像。通过从更少测量数据中获取高质量重建，PPTV为开发更紧凑、高效、经济的片上无透镜显微系统开辟道路，在数字病理学、内窥镜检查、床旁诊断及高内涵筛选等领域具有应用潜力。