Evaluating autofocusing metrics in digital lensless holographic microscopy

This study proposes a novel approach in response to the persistent challenge of achieving precise autofocus in Digital Lensless Holographic Microscopy (DLHM). It involves employing an enhanced Bluestein algorithm to simulate DLHM holograms under a variety of conditions, spanning amplitude-only, phase-only, and amplitude-phase objects. These simulated holograms are used to assess the performance of autofocus metrics, including the Dubois and Spectral Dubois metrics, gradient and variance-based approaches, and lastly a learning-based model. By considering the variety of sample types and geometrical configurations, this study delves into the robustness and limitations of these metrics across diverse scenarios. This research reveals different performances depending on sample characteristics, offering valuable insights into selecting the most suitable autofocus metric, which is a demanding step in practical DLHM applications.