Automatic multifocusing in digital holographic microscopy.

Holography captures the full three-dimensional optical field of a sample in a single intensity measurement. This enables numerical refocusing to arbitrary axial planes and, in principle, automatic multifocusing in digital holographic microscopy (DHM). Existing work on holographic autofocusing, however, largely targets the simpler single-plane setting and typically relies either on computationally expensive depth sweeps with hand-crafted sharpness metrics or on deep-learning models trained for a single focal distance per hologram. We address the more practical and challenging problem of automatic multifocusing for in-line flow-through DHM, where heterogeneous microscopic objects are distributed across multiple axial depths within a single hologram. We propose a two-stage pipeline for real-world DHM applications. First, a coarse, low-resolution 3D search detects object locations and provides approximate lateral and axial positions using a robust focus metric on downscaled holograms. Second, a deep-learning-based refinement stage processes the resulting object ROIs and predicts the residual defocus to obtain precise axial positions. Within this framework, we compare regression and classification formulations, introduce classification models with non-uniform depth bins that concentrate capacity near focus, and examine recursive refinement. Evaluated on a practical and diverse dataset of living micro-organisms, the proposed system achieves accurate, data-efficient, robust, and computationally efficient multifocusing, making it suitable for quasi-real-time DHM applications.