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29 April 2022 Fourier phase retrieval algorithm based on deep denoiser network
Mingguang Shan, Jiaqi Jing
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Proceedings Volume 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022); 122471U (2022) https://doi.org/10.1117/12.2636839
Event: 2022 International Conference on Image, Signal Processing, and Pattern Recognition, 2022, Guilin, China
Abstract
Fourier phase recovery techniques focus on how to reconstruct object information from phaseless measurement. Generally, such model-based phase recovery algorithms are difficult to obtain high-quality reconstructions in the presence of noise interference. Hence, we proposed a phase retrieval algorithm with deep denoiser networks. Firstly, an optimization model is constructed for the phase retrieval problem, then the alternating direction method of multipliers method is used to solve optimization problem iteratively. Besides, a well-trained deep neural network act as plug-and-play denoiser to participate the process of algorithm. Our method combines the model information of traditional phase retrieval algorithm and the fitting ability of the deep neural network, experiments show that it can achieve higher reconstruction result in the face of noisy image, and the generalization ability is also improved compared to end-to-end method.
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Mingguang Shan and Jiaqi Jing "Fourier phase retrieval algorithm based on deep denoiser network", Proc. SPIE 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022), 122471U (29 April 2022); https://doi.org/10.1117/12.2636839
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KEYWORDS
Phase retrieval
Neural networks
Denoising
Model-based design
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