Wide-field high-resolution microscopy via high-speed galvo scanning and real-time mosaicking

Fuente: arXiv
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Auteurs principaux: Huang, Ziyi, Xiong, Rosalinda, Wang, Yaning, Yu, Jinglun, Kang, Jin U.
Format: Preprint
Publié: 2026
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author Huang, Ziyi
Xiong, Rosalinda
Wang, Yaning
Yu, Jinglun
Kang, Jin U.
author_facet Huang, Ziyi
Xiong, Rosalinda
Wang, Yaning
Yu, Jinglun
Kang, Jin U.
contents Wide-field high-resolution microscopy requires fast scanning and accurate image mosaicking to cover large fields of view without compromising image quality. However, conventional galvanometric scanning, particularly under sinusoidal driving, can introduce nonuniform spatial sampling, leading to geometric inconsistencies and brightness variations across the scanned field. To address these challenges, we present an image mosaicking framework for wide-field microscopic imaging that is applicable to both linear and sinusoidal galvanometric scanning strategies. The proposed approach combines a translation-based geometric mosaicking model with region-of-interest (ROI) based brightness correction and seam-aware feathering to improve radiometric consistency across large fields of view. The method relies on calibrated scan parameters and synchronized scan--camera control, without requiring image-content-based registration. Using the proposed framework, wide-field mosaicked images were successfully reconstructed under both linear and sinusoidal scanning strategies, achieving a field of view of up to $2.5 \times 2.5~\mathrm{cm}^2$ with a total acquisition time of approximately $6~\mathrm{s}$ per dataset. Quantitative evaluation shows that both scanning strategies demonstrate improved image quality, including enhanced brightness uniformity, increased contrast-to-noise ratio (CNR), and reduced seam-related artifacts after image processing, while preserving a lateral resolution of $7.81~μ\mathrm{m}$. Overall, the presented framework provides a practical and efficient solution for scan-based wide-field microscopic mosaicking.
format Preprint
id arxiv_https___arxiv_org_abs_2602_02758
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Wide-field high-resolution microscopy via high-speed galvo scanning and real-time mosaicking
Huang, Ziyi
Xiong, Rosalinda
Wang, Yaning
Yu, Jinglun
Kang, Jin U.
Image and Video Processing
Wide-field high-resolution microscopy requires fast scanning and accurate image mosaicking to cover large fields of view without compromising image quality. However, conventional galvanometric scanning, particularly under sinusoidal driving, can introduce nonuniform spatial sampling, leading to geometric inconsistencies and brightness variations across the scanned field. To address these challenges, we present an image mosaicking framework for wide-field microscopic imaging that is applicable to both linear and sinusoidal galvanometric scanning strategies. The proposed approach combines a translation-based geometric mosaicking model with region-of-interest (ROI) based brightness correction and seam-aware feathering to improve radiometric consistency across large fields of view. The method relies on calibrated scan parameters and synchronized scan--camera control, without requiring image-content-based registration. Using the proposed framework, wide-field mosaicked images were successfully reconstructed under both linear and sinusoidal scanning strategies, achieving a field of view of up to $2.5 \times 2.5~\mathrm{cm}^2$ with a total acquisition time of approximately $6~\mathrm{s}$ per dataset. Quantitative evaluation shows that both scanning strategies demonstrate improved image quality, including enhanced brightness uniformity, increased contrast-to-noise ratio (CNR), and reduced seam-related artifacts after image processing, while preserving a lateral resolution of $7.81~μ\mathrm{m}$. Overall, the presented framework provides a practical and efficient solution for scan-based wide-field microscopic mosaicking.
title Wide-field high-resolution microscopy via high-speed galvo scanning and real-time mosaicking
topic Image and Video Processing
url https://arxiv.org/abs/2602.02758