Solving ptychography with a convex relaxation.

Journal Article (Journal Article)

Ptychography is a powerful computational imaging technique that transforms a collection of low-resolution images into a high-resolution sample reconstruction. Unfortunately, algorithms that currently solve this reconstruction problem lack stability, robustness, and theoretical guarantees. Recently, convex optimization algorithms have improved the accuracy and reliability of several related reconstruction efforts. This paper proposes a convex formulation of the ptychography problem. This formulation has no local minima, it can be solved using a wide range of algorithms, it can incorporate appropriate noise models, and it can include multiple a priori constraints. The paper considers a specific algorithm, based on low-rank factorization, whose runtime and memory usage are near-linear in the size of the output image. Experiments demonstrate that this approach offers a 25% lower background variance on average than alternating projections, the ptychographic reconstruction algorithm that is currently in widespread use.

Full Text

Duke Authors

Cited Authors

  • Horstmeyer, R; Chen, RY; Ou, X; Ames, B; Tropp, JA; Yang, C

Published Date

  • May 2015

Published In

Volume / Issue

  • 17 /

Start / End Page

  • 053044 -

PubMed ID

  • 26146480

Pubmed Central ID

  • PMC4486359

Electronic International Standard Serial Number (EISSN)

  • 1367-2630

International Standard Serial Number (ISSN)

  • 1367-2630

Digital Object Identifier (DOI)

  • 10.1088/1367-2630/17/5/053044

Language

  • eng