Quantitative phase imaging based on model transfer learning

Abstract

Convolutional neural networks have been widely used in optical information processing and the generalization ability of the network depends greatly on the scale and diversity of the datasets, however, the acquisition of mass datasets and later annotation have become a common problem that hinders its further progress. In this study, a model transfer-based quantitative phase imaging (QPI) method is proposed, which fine-tunes the network parameters through loading pre-training base model and transfer learning, enable the network with good generalization ability. Most importantly, a feature fusion method based on moment reconstruction is proposed for training dataset generation, which can construct rich enough datasets that can cover most situations and accurately annotated, it fundamentally solves the problem from the scale and representational ability of the datasets. Besides, a feature distribution distance scoring (FDDS) rule is proposed to evaluate the rationality of the constructed datasets. The experimental results show that this method is suitable for different types of samples to achieve fast and high-accuracy phase imaging, which greatly relieves the pressure of data, tagging and generalization ability in the data-driven method.