NeuPh:scalable and generalizable neural phase retrieval with local conditional neural fields  

作　　者：Hao Wang Jiabei Zhu Yunzhe Li Qianwan Yang Lei Tian 

机构地区：[1]Boston University,Department of Electrical and Computer Engineering,Boston,Massachusetts,United States [2]Boston University,Department of Biomedical Engineering,Boston,Massachusetts,United States

出　　处：《Advanced Photonics Nexus》2024年第5期67-76,共10页先进光子学通讯（英文）

基　　金：supported by the National Science Foundation(Grant No.1846784).

摘　　要：Deep learning has transformed computational imaging,but traditional pixel-based representations limit their ability to capture continuous multiscale object features.Addressing this gap,we introduce a local conditional neural field(LCNF)framework,which leverages a continuous neural representation to provide flexible object representations.LCNF’s unique capabilities are demonstrated in solving the highly ill-posed phase retrieval problem of multiplexed Fourier ptychographic microscopy.Our network,termed neural phase retrieval(NeuPh),enables continuous-domain resolution-enhanced phase reconstruction,offering scalability,robustness,accuracy,and generalizability that outperform existing methods.NeuPh integrates a local conditional neural representation and a coordinate-based training strategy.We show that NeuPh can accurately reconstruct high-resolution phase images from low-resolution intensity measurements.Furthermore,NeuPh consistently applies continuous object priors and effectively eliminates various phase artifacts,demonstrating robustness even when trained on imperfect datasets.Moreover,NeuPh improves accuracy and generalization compared with existing deep learning models.We further investigate a hybrid training strategy combining both experimental and simulated datasets,elucidating the impact of domain shift between experiment and simulation.Our work underscores the potential of the LCNF framework in solving complex large-scale inverse problems,opening up new possibilities for deep-learning-based imaging techniques.

关 键 词：neural representation phase retrieval computational imaging deep learning computational microscopy 

分 类 号：O17[理学—数学]