Paper Info
Title
Physics-based Iterative Projection Complex Neural Network for Phase Retrieval in Lensless Microscopy Imaging
Abstract
Phase retrieval from intensity-only measurements plays a central role in many real-world imaging tasks. In recent years, deep neural networks based methods emerge and show promising performance for phase retrieval. However, their interpretability and generalization still remain a major challenge. In this paper, we propose to combine the advantages of both model-based alternative projection method and deep neural network for phase retrieval, so as to achieve network interpretability and inference effectiveness simultaneously. Specifically, we unfold the iterative process of the alternative projection phase retrieval into a feed-forward neural network, whose layers mimic the processing flow. The physical model of the imaging process is then naturally embedded into the neural network structure. Moreover, a complex-valued U-Net is proposed for defining image priori for forward and backward projection in dual planes. Finally, we designate physics-based formulation as an untrained deep neural network, whose weights are enforced to fit to the given intensity measurements. In summary, our scheme for phase retrieval is effective, interpretable, physics-based and unsupervised. Experimental results demonstrate that our method achieves superior performance compared with the state-of-the-arts in a practical phase retrieval application-lensless microscopy imaging.
Year
DOI
Venue
2021
10.1109/CVPR46437.2021.01038
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
DocType
ISSN
Citations 
Conference
1063-6919
0
PageRank 
References 
Authors
0.34
5
6
Name
Order
Citations
PageRank
Feilong Zhang100.34
Xianming Liu246147.55
Cheng Guo300.34
Shiyi Lin400.34
Junjun Jiang5113874.49
Xiangyang Ji653373.14