Plug-and-Play Volumetric Reconstruction for Compressive Sensing Light-Sheet Microscopy

2026-07-02 · Source: Computer Vision and Pattern Recognition · Field: Science & Research — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences, Health & Medical Research · Depth: Expert, quick

Summary

A plug-and-play (PnP) framework is proposed for volumetric reconstruction in compressive sensing light-sheet microscopy (CS-LSM), a technique enabling fast volumetric imaging by encoding multiple axial planes into each camera exposure. This framework flexibly incorporates any user-specified denoiser to recover underlying volumes from highly multiplexed measurements. Building on a slice-based formulation, it introduces an axial-coupled model that exploits correlations between adjacent slices to enhance volumetric continuity. For computational efficiency, the framework employs a Woodbury-based update for the data-consistency step and a Gauss-Seidel sweep for denoising in the axial-coupled model. Subsequential convergence of the algorithm is established under a weakly convex regularization assumption. Experiments on synthetic and real zebrafish-heart data confirm its success in recovering cellular structures and provide insights into denoiser performance within the CS-LSM setup.

Key takeaway

For Research Scientists developing fast volumetric imaging solutions for microscopy, this plug-and-play (PnP) framework offers a robust approach to reconstruct highly multiplexed compressive sensing light-sheet microscopy (CS-LSM) data. You should consider integrating this flexible framework to leverage custom denoisers and improve volumetric continuity in your imaging pipelines, as it provides a computationally efficient method for recovering cellular structures from compressed measurements.

Key insights

The PnP framework enables flexible, efficient volumetric reconstruction for CS-LSM by integrating custom denoisers and exploiting axial correlations.

Principles

• Incorporate user-specified denoisers flexibly.
• Exploit axial correlations for volumetric continuity.
• Use Woodbury-based updates for efficiency.

Method

The PnP framework uses a slice-based formulation, an axial-coupled model, Woodbury-based updates for data-consistency, and a Gauss-Seidel sweep for denoising.

In practice

• Recover cellular structures from compressed measurements.
• Evaluate denoiser performance in CS-LSM.
• Apply to synthetic and real zebrafish-heart data.

Topics

• Compressive Sensing Microscopy
• Light-Sheet Microscopy
• Volumetric Reconstruction
• Plug-and-Play Frameworks
• Image Denoising
• Zebrafish Heart Imaging

Best for: AI Scientist, Research Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.