Learning a model-based neural network for quantitative phase imaging based on the transport of intensity

Xiaofeng Wu; Ziling Wu; Yunhui Zhu

doi:10.1117/12.2558361

21 May 2020 Learning a model-based neural network for quantitative phase imaging based on the transport of intensity

Xiaofeng Wu, Ziling Wu, Yunhui Zhu

Proceedings Volume 11396, Computational Imaging V; 1139604 (2020) https://doi.org/10.1117/12.2558361
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Quantitative phase imaging (QPI) provides enhanced contrast for weakly absorbing specimens such as biological tissues under optical light and soft materials under X-ray. In this work, we develop a model-based phase retrieval framework by integrating the physics principles of phase imaging with the deep learning-based approach. Both measurements and the forward model are used as the inputs for a model-based neural network. The features of the object and the regularization weight of the established priors are learned by minimizing the difference between the network output to the ground truth during the training process. This method is tested on phase imaging of handwriting digital patterns and biological cells in a simulation of propagation-based TIE (transport of intensity equation) phase retrieval. We achieve enhanced accuracy for the phase retrieval compared to non-model based end-to-end neural networks and reduce the computation cost compared to traditional model-based iterative reconstruction algorithms.

Citation Download Citation

Xiaofeng Wu, Ziling Wu, and Yunhui Zhu "Learning a model-based neural network for quantitative phase imaging based on the transport of intensity", Proc. SPIE 11396, Computational Imaging V, 1139604 (21 May 2020); https://doi.org/10.1117/12.2558361

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