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On-line Access: 2025-06-25
Received: 2025-01-08
Revision Accepted: 2025-03-06
Crosschecked: 2025-06-25
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Citations: Bibtex RefMan EndNote GB/T7714
Seeing the macro in the micro: a diffusion model-based approach for style transfer in cellular images
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Jiayi CAI, Yong HE, Feng LIU, Byung-Ho KANG, Xuping FENG. Seeing the macro in the micro: a diffusion model-based approach for style transfer in cellular images[J]. Journal of Zhejiang University Science B, 2025, 26(6): 609-612.
@article{title="Seeing the macro in the micro: a diffusion model-based approach for style transfer in cellular images",
author="Jiayi CAI, Yong HE, Feng LIU, Byung-Ho KANG, Xuping FENG",
journal="Journal of Zhejiang University Science B",
volume="26",
number="6",
pages="609-612",
year="2025",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B2500012"
}
%0 Journal Article
%T Seeing the macro in the micro: a diffusion model-based approach for style transfer in cellular images
%A Jiayi CAI
%A Yong HE
%A Feng LIU
%A Byung-Ho KANG
%A Xuping FENG
%J Journal of Zhejiang University SCIENCE B
%V 26
%N 6
%P 609-612
%@ 1673-1581
%D 2025
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B2500012
TY - JOUR
T1 - Seeing the macro in the micro: a diffusion model-based approach for style transfer in cellular images
A1 - Jiayi CAI
A1 - Yong HE
A1 - Feng LIU
A1 - Byung-Ho KANG
A1 - Xuping FENG
J0 - Journal of Zhejiang University Science B
VL - 26
IS - 6
SP - 609
EP - 612
%@ 1673-1581
Y1 - 2025
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B2500012
Abstract: The internal structures of cells as the basic units of life are a major wonder of the microscopic world. cellular images provide an intriguing window to help explore and understand the composition and function of these structures. Scientific imagery combined with artistic expression can further expand the potential of imaging in educational dissemination and interdisciplinary applications. This study presents an innovative diffusion model-based approach for style transfer in cellular images, combining scientific rigor with artistic expression. By leveraging training-free large-scale pre-trained diffusion models, the proposed method integrates the intricate morphological and textural features of cellular images with diverse artistic styles. Key techniques such as the inversion of denoising diffusion implicit models (DDIMs), adaptive instance normalization (AdaIN), self-attention style injection, and attention temperature scaling ensure the preservation of cellular structures while enhancing visual expressiveness. The results showcase the potential of this strategy for interdisciplinary applications, enriching both the visualization and educational dissemination of cellular imagery through compelling storytelling and aesthetic appeal.
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