论文标题
克服信息减少了数据和实验性不确定参数，并具有正则优化
Overcoming information reduced data and experimentally uncertain parameters in ptychography with regularized optimization
论文作者
论文摘要
ptychography的数学问题的过度确定性通过了许多实验性更高的设置减少。此外，样品诱导的相移的重建通常受到实验参数和有限样品厚度的不确定性的限制。提出的是一种共轭梯度下降算法，是对PtyChography（ROP）的正则优化，可恢复部分已知的实验参数以及相位变化，通过将多粘膜形式纳入有限样品厚度，包括在优化过程中的正则化，从而从可靠的结果中纳入了可靠的结果，从而改善了分辨率，从而得到了严格的信息，并获得了可靠的结果。
The overdetermination of the mathematical problem underlying ptychography is reduced by a host of experimentally more desirable settings. Furthermore, reconstruction of the sample-induced phase shift is typically limited by uncertainty in the experimental parameters and finite sample thicknesses. Presented is a conjugate gradient descent algorithm, regularized optimization for ptychography (ROP), that recovers the partially known experimental parameters along with the phase shift, improves resolution by incorporating the multislice formalism to treat finite sample thicknesses, and includes regularization in the optimization process, thus achieving reliable results from noisy data with severely reduced and underdetermined information.
