TY  - EJOUR
AU  - Bangun, Arya
AU  - Töllner, Maximilian
AU  - Zhao, Xuan
AU  - Kübel, Christian
AU  - Scharr, Hanno
TI  - FlowTIE: Flow-based Transport of Intensity Equation for Phase Gradient Estimation from 4D-STEM Data
PB  - arXiv
M1  - FZJ-2025-04917
PY  - 2025
AB  - 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.
KW  - Machine Learning (cs.LG) (Other)
KW  - FOS: Computer and information sciences (Other)
LB  - PUB:(DE-HGF)25
DO  - DOI:10.48550/ARXIV.2511.07633
UR  - https://juser.fz-juelich.de/record/1048804
ER  -