Overcoming information reduced data and experimentally uncertain parameters in ptychography with regularized optimization
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 09, 2020
- Source ID
- 10.1364/oe.396925
Entities
People
- Christoph Tobias Koch
- David Anthony Muller
- Marcel Schloz
- Thomas Christopher Pekin
- Wouter Van Den Broek
- Zhen Chen
Organizations
- Defense Advanced Research Projects Agency
- German Research Foundation
- National Science Foundation