Automatic phase aberration compensation in digital holographic microscopy based on adaptive-threshold M-estimator sample consensus.

In digital holographic microscopy, optical-system-induced phase aberrations affect phase measurement. To compensate, we propose an accurate phase-aberration compensation method that uses M-estimator sample consensus to classify the background phase. Field-of-view and noise thresholds are introduced to handle strong aberrations at the field-of-view edges and optical noise, improving classification accuracy. Simulations and experiments demonstrate that the proposed method can effectively separate the sample morphology from the background, achieving higher phase reconstruction accuracy than conventional algorithms combining dual fitting with background segmentation and those combining curve fitting with background segmentation.