A Plug-and-Play Framework for Volumetric Light-Sheet Image Reconstruction
This work addresses the challenge of high-speed, low-light biological imaging for cardiac studies, representing an incremental improvement by combining existing methods with new regularization techniques.
The paper tackled the problem of capturing dynamic cellular structure in the beating heart by proposing a computational imaging framework that integrates Compressive Sensing with Light-Sheet Microscopy, achieving successful reconstruction of cellular structures with excellent denoising performance and image clarity in zebrafish heart imaging under high compression ratios.
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.