  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.

Image and Video Processing;Information Retrieval;Optics