Preprint

FZJ-2025-04917

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png FlowTIE: Flow-based Transport of Intensity Equation for Phase Gradient Estimation from 4D-STEM Data

Bangun, A.FZJ* ; Töllner, M. ; Zhao, X.FZJ* ; Kübel, C. ; Scharr, H.FZJ*

2025
arXiv

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Please use a persistent id in citations: doi:10.48550/ARXIV.2511.07633  doi:10.34734/FZJ-2025-04917

Abstract: We introduce FlowTIE, a neural-network-based framework for phase reconstruction from 4D-Scanning Transmission Electron Microscopy (STEM) data, which integrates the Transport of Intensity Equation (TIE) with a flow-based representation of the phase gradient. This formulation allows the model to bridge data-driven learning with physics-based priors, improving robustness under dynamical scattering conditions for thick specimen. The validation on simulated datasets of crystalline materials, benchmarking to classical TIE and gradient-based optimization methods are presented. The results demonstrate that FlowTIE improves phase reconstruction accuracy, fast, and can be integrated with a thick specimen model, namely multislice method.

Keyword(s): Machine Learning (cs.LG) ; FOS: Computer and information sciences

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Contributing Institute(s):

1. Datenanalyse und Maschinenlernen (IAS-8)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2025

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Database coverage:

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