Coherent modulation imaging using a physics-driven neural network

Coherent modulation imaging (CMI) is a lessness diffraction imaging technique, which uses an iterative algorithm to reconstruct a complex field from a single intensity diffraction pattern. Deep learning as a powerful optimization method can be used to solve highly ill-conditioned problems, including complex field phase retrieval. In this study, a physics-driven neural network for CMI is developed, termed CMINet, to reconstruct the complex-valued object from a single diffraction pattern. The developed approach optimizes the network's weights by a customized physical-model-based loss function, instead of using any ground truth of the reconstructed object for training beforehand. Simulation experiment results show that the developed CMINet has a high reconstruction quality with less noise and robustness to physical parameters. Besides, a trained CMINet can be used to reconstruct a dynamic process with a fast speed instead of iterations frame-by-frame. The biological experiment results show that CMINet can reconstruct high-quality amplitude and phase images with more sharp details, which is practical for biological imaging applications.