Phase unwrapping of digital holographic microscopy using adaptive region segmentation and phase derivative calibration with respect to fringe density

Abstract Accurate phase unwrapping of the wrapped phase maps with high noise and uneven fringe densities is an essential problem for phase restoration in digital holographic microscopy. In this paper, a phase unwrapping algorithm using adaptive region segmentation and phase derivative calibration with respect to fringe density is proposed. The dense and sparse fringe regions of the wrapped phase map are adaptively segmented by using the sine/cosine spatial filtering and calculating the second-order phase gradient. Then, the first-order spatial wrapped phase derivative maps are divided into four phase derivative maps, and the phase anomalies on them are calibrated respectively. Finally, the four maps were recombined into two new phase derivative maps, and the unweighted least-squares iterative algorithm is implemented to obtain the continuous phase map. The advantage of our algorithm is that it can detect, calibrate and restore the phase anomalies based on fringe density automatically. Simulation and experimental results demonstrated that the proposed algorithm has better accuracy and effectiveness in dealing with the wrapped phase maps with high fringe density and high noise, which indicates that our algorithm is able to process the wrapped phase maps corrupted by coherent noise and phase aberration in the digital holographic microscopy.