Rapid quantitative phase imaging using deep learning for phase object with refractive index variation

Abstract

In quantitative phase imaging (QPI), it is greatly important to extract the phase from the phase-shifting interferograms. Despite extensive research efforts for decades, how to retrieve the actual phase using the minimum number of interferograms, continues to be an important problem. To cope with this problem, a deep-learning-based method of phase extraction is proposed in QPI. After the fringe pattern features of interferograms associated with phase retrieval are extracted, the proposed approach can establish the pixel-level mapping relation between the interferograms and ground-truth phases so that it can rapidly recover the true phase, without phase unwrapping, from one-frame interferogram. The feasibility and applicability of this method are demonstrated, respectively, by the datasets of the microsphere, neuronal cell with refractive index variation and red blood cell. The results show that this method has obvious advantages in terms of phase extraction, compared with the traditional phase retrieval algorithms.