Projected Multi-Agent Consensus Equilibrium (PMACE) With Application to Ptychography

Journal Article · 30 October 2023 · IEEE Transactions on Computational Imaging

DOI: https://doi.org/10.1109/tci.2023.3328288 · OSTI ID:2217508

Zhai, Qiuchen  ^[1]; Buzzard, Gregery T.  ^[1]; Mertes, Kevin  ^[2]; Wohlberg, Brendt  ^[2]; Bouman, Charles A.  ^[1]

---

1. Purdue University, West Lafayette, IN (United States)
2. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Multi-Agent Consensus Equilibrium (MACE) formulates an inverse imaging problem as a balance among multiple update agents such as data-fitting terms and denoisers. However, each such agent operates on a separate copy of the full image, leading to redundant memory use and slow convergence when each agent affects only a small subset of the full image. In this article, we extend MACE to Projected Multi-Agent Consensus Equilibrium (PMACE), in which each agent updates only a projected component of the full image, thus greatly reducing memory use for some applications. We describe PMACE in terms of an equilibrium problem and an equivalent fixed-point problem and show that in most cases the PMACE equilibrium is not the solution of an optimization problem. To demonstrate the value of PMACE, we apply it to the problem of ptychography, in which a sample is reconstructed from the diffraction patterns resulting from coherent X-ray illumination at multiple overlapping spots. In our PMACE formulation, each spot corresponds to a separate data-fitting agent, with the final solution found as an equilibrium among all the agents. In conclusion, our results demonstrate that the PMACE reconstruction algorithm generates more accurate reconstructions at a lower computational cost than existing ptychography algorithms when the spots are sparsely sampled.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF)
• Grant/Contract Number: 89233218CNA000001; 20200061DR; CCF-1763896
• OSTI ID: 2217508
• Report Number(s): LA-UR-23-22951
• Journal Information: IEEE Transactions on Computational Imaging, Vol. 9; ISSN 2573-0436
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English