% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@ARTICLE{Bangun:1048804,
author = {Bangun, Arya and Töllner, Maximilian and Zhao, Xuan and
Kübel, Christian and Scharr, Hanno},
title = {{F}low{TIE}: {F}low-based {T}ransport of {I}ntensity
{E}quation for {P}hase {G}radient {E}stimation from
4{D}-{STEM} {D}ata},
publisher = {arXiv},
reportid = {FZJ-2025-04917},
year = {2025},
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.},
keywords = {Machine Learning (cs.LG) (Other) / FOS: Computer and
information sciences (Other)},
cin = {IAS-8},
cid = {I:(DE-Juel1)IAS-8-20210421},
pnm = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
and Research Groups (POF4-511)},
pid = {G:(DE-HGF)POF4-5112},
typ = {PUB:(DE-HGF)25},
doi = {10.48550/ARXIV.2511.07633},
url = {https://juser.fz-juelich.de/record/1048804},
}