In ptychographic imaging, the trade-off between the number of acquisitions and the resultant imaging quality presents a complex optimization problem. Increasing the number of acquisitions typically yields reconstructions with higher spatial resolution and finer details. Conversely, a reduction in measurement frequency often compromises the quality of the reconstructed images, manifesting as increased noise and coarser details. To address this challenge, we employ sparsity priors to reformulate the ptychographic reconstruction task as a total variation regularized optimization problem. We introduce a new computational framework, termed the ptychographic proximal total-variation (PPTV) solver, designed to integrate into existing ptychography settings without necessitating hardware modifications. Through comprehensive numerical simulations, we validate that PPTV-driven coded ptychography is capable of producing highly accurate reconstructions with a minimal set of eight intensity measurements. Convergence analysis further substantiates the robustness, stability, and computational feasibility of the proposed PPTV algorithm. Experimental results obtained from optical setups unequivocally demonstrate that the PPTV algorithm facilitates high-throughput, high-resolution imaging while significantly reducing the measurement burden. These findings indicate that the PPTV algorithm has the potential to substantially mitigate the resource-intensive requirements traditionally associated with high-quality ptychographic imaging, thereby offering a pathway toward the development of more compact and efficient ptychographic microscopy systems.

翻译：在叠层成像中，采集次数与最终成像质量之间的权衡构成了一个复杂的优化问题。增加采集次数通常能获得更高空间分辨率和更精细细节的重建结果，而减少测量频次则往往以噪声增加和细节粗糙为代价，导致重建图像质量下降。为应对这一挑战，我们利用稀疏先验将叠层重建任务重新表述为全变分正则化优化问题。我们提出了一种新型计算框架——叠层近端全变分求解器（PPTV），该求解器可直接集成至现有叠层成像装置中，无需硬件改造。通过全面的数值模拟验证，采用PPTV驱动的编码叠层成像仅需八次强度测量即可实现高精度重建。收敛性分析进一步证明了所提PPTV算法的鲁棒性、稳定性和计算可行性。基于光学装置的实验结果明确表明，PPTV算法在显著降低测量负担的同时，实现了高通量、高分辨率成像。这些发现表明，PPTV算法有望大幅缓解传统高质量叠层成像对大量资源的需求，从而为开发更紧凑高效的叠层显微成像系统开辟了新路径。