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

TL;DR

This paper introduces a robust image mosaicking framework for wide-field high-resolution microscopy that effectively handles both linear and sinusoidal galvanometric scanning, ensuring uniform brightness and high image quality over large areas.

Contribution

It presents a novel mosaicking approach combining geometric modeling and brightness correction that does not depend on image content-based registration, applicable to different scanning strategies.

Findings

Achieved a 2.5x2.5 cm^2 field of view in 6 seconds.

Improved brightness uniformity and contrast-to-noise ratio.

Reduced seam artifacts while maintaining 7.81 μm resolution.

Abstract

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 $7.81~\mu\mathrm{m}$. Overall, the presented framework provides a practical and efficient solution for scan-based wide-field microscopic mosaicking.