A Plug-and-Play Framework for Volumetric Light-Sheet Image Reconstruction

TL;DR

This paper introduces a computational imaging framework combining compressive sensing and plug-and-play denoisers for high-speed, low-phototoxic 3D cardiac imaging with light-sheet microscopy, enabling detailed cellular reconstruction.

Contribution

It presents a novel plug-and-play reconstruction method integrating advanced denoisers and temporal regularization for volumetric light-sheet image reconstruction.

Findings

Successful reconstruction of zebrafish heart images at high compression ratios

Enhanced image clarity and structural preservation in dynamic biological imaging

Robust performance demonstrated in real-world high-speed, low-light scenarios

Abstract

Cardiac contraction is a rapid, coordinated process that unfolds across three-dimensional tissue on millisecond timescales. Traditional optical imaging is often inadequate for capturing dynamic cellular structure in the beating heart because of a fundamental trade-off between spatial and temporal resolution. To overcome these limitations, we propose a high-performance computational imaging framework that integrates Compressive Sensing (CS) with Light-Sheet Microscopy (LSM) for efficient, low-phototoxic cardiac imaging. The system performs compressed acquisition of fluorescence signals via random binary mask coding using a Digital Micromirror Device (DMD). We propose a Plug-and-Play (PnP) framework, solved using the alternating direction method of multipliers (ADMM), which flexibly incorporates advanced denoisers, including Tikhonov, Total Variation (TV), and BM3D. To preserve structural continuity in dynamic imaging, we further introduce temporal regularization enforcing smoothness between adjacent z-slices. Experimental results on zebrafish heart imaging under high compression ratios demonstrate that the proposed method successfully reconstructs cellular structures with excellent denoising performance and image clarity, validating the effectiveness and robustness of our algorithm in real-world high-speed, low-light biological imaging scenarios.