Spatio-temporal microfluidic reconstruction via compressive dark-field digital holography

Digital holography is a powerful technique for the four-dimensional (4D) spatio-temporal reconstruction of microparticles in microfluidic systems, which is crucial for applications in fluid dynamics and biology. However, the accuracy of conventional in-line holography is often hindered by significant background noise and artifacts. To overcome these limitations, we propose what is believed to be a novel compressive dark-field digital holography (CD-DH) method. This approach integrates a dark-field operation to enhance the signal-to-noise ratio (SNR) by suppressing background interference, and leverages compressed sensing (CS) with a total variation (TV) regularization constraint to computationally eliminate the twin image problem, enabling high-precision 4D reconstruction from a sequence of consecutively recorded holograms. We systematically validated the performance of our method through simulations and experiments. In simulations, CD-DH reduced the mean absolute error (MAE) and root mean square error (RMSE) of particle localization by approximately 67.9% and 67.5%, respectively, compared to the conventional back-propagation (BP) algorithm. Experimentally, when analyzing polystyrene microspheres in a uniform flow, our method achieved a velocity measurement error of 2%, a stark improvement over the error from the BP method. Furthermore, in a non-uniform flow within a Y-shaped microfluidic chip, our technique effectively removed background artifacts and accurately reconstructed particle trajectories and velocity fields that align well with Poiseuille flow theory. These results demonstrate that CD-DH is a robust and precise tool, providing powerful technical support for the quantitative visualization of complex micro-scale flow phenomena.