Phase retrieval based on convolutional neural network

AoYa Li; ChuanZhao Zhang; QuanBing Zhang; SuFan Wang

doi:10.1117/12.2606804

24 November 2021 Phase retrieval based on convolutional neural network

AoYa Li, ChuanZhao Zhang, QuanBing Zhang, SuFan Wang

Proceedings Volume 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations; 1206914 (2021) https://doi.org/10.1117/12.2606804
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

Phase retrieval is of great significance in the fields of medical imaging and computational holography. To solve the problem more efficiently, this paper presents a phase recovery network with two modes of operation based on the convolution neural network, which not only can get persistent model by training data set, but also can build a special loss function to recover the unknown signal in a self-optimized way without data set in the case of Gaussian measurement model. Comparison of the simulation results show that the network is able to obtain better results with fewer measurements than the existing phase recovery algorithms.

Citation Download Citation

AoYa Li, ChuanZhao Zhang, QuanBing Zhang, and SuFan Wang "Phase retrieval based on convolutional neural network", Proc. SPIE 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations, 1206914 (24 November 2021); https://doi.org/10.1117/12.2606804

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KEYWORDS

Phase retrieval

Data modeling

Convolutional neural networks

Signal processing

Image processing algorithms and systems

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