Deep learning framework for analyzing birefringence imaging by incorporating optical polarization overlap in stress-induced ferroelectric SrTiO3

Optical microscopy is vital in many scientific fields, and various super-resolution techniques have been developed to overcome the resolution limit that restricts the separation of spatially mixed light. However, conventional methods inherently cannot resolve overlapping optical polarization (OP) co...

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Vydané v:Science and technology of advanced materials. Methods Ročník 5; číslo 1
Hlavní autori: Hirotaka Manaka, Shoutarou Katayama, Soichiro Honda, Yoko Miura
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Taylor & Francis Group 31.12.2025
Predmet:
3D convolutional autoencoder
LSTM
SrTiO3
stress-induced ferroelectricity
temperature series forest
temperature series principal component analysis
ISSN:2766-0400
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Shrnutí:Optical microscopy is vital in many scientific fields, and various super-resolution techniques have been developed to overcome the resolution limit that restricts the separation of spatially mixed light. However, conventional methods inherently cannot resolve overlapping optical polarization (OP) components, limiting the ‘polarization resolution’ in polarized light microscopy. Instead of quantitatively evaluating ‘polarization resolution’, this study aims to reliably separate intrinsic OP states based on consistent clustering results that are robust to variations in the spatial receptive field (SRF) size. We integrate statistical analysis, machine learning, and deep learning to evaluate overlapping OP states in temperature-dependent birefringence imaging of the stress-induced ferroelectric SrTiO[Formula: see text] under an external force of 231 MPa. A long short-term memory (LSTM) network is used to extract temperature-dependent features from sequential image data, which effectively captures subtle changes in structural and ferroelectric phase transitions. A 3D convolutional autoencoder (3DCAE) learns spatial relationships between adjacent pixels from these temperature-dependent features, addressing OP overlap at different spatial scales based on different SRF sizes. Although the 3DCAE output considerably depends on the SRF size, clustering results obtained via temperature series forest (Tsf) analysis are highly consistent. This robustness indicates that the extracted OP states reflect physically meaningful spatial distributions rather than convolution artifacts. The proposed sequential analytical framework successfully reconstructs intrinsic OP distributions while balancing local and global structural features, providing a robust foundation for OP-sensitive imaging in materials science.
ISSN:2766-0400
DOI:10.1080/27660400.2025.2568376