SiPRNet: End-to-End Learning for Single-Shot Phase Retrieval

Traditional optimization algorithms have been developed to deal with the phase retrieval problem. However, multiple measurements with different random or non-random masks are needed for giving a satisfactory performance. This brings a burden to the implementation of the algorithms in practical systems. Even worse, expensive optical devices are required to implement the optical masks. Recently, deep learning, especially convolutional neural networks (CNN), has played important roles in various image reconstruction tasks. However, traditional CNN structure fails to reconstruct the original images from their Fourier measurements because of tremendous domain discrepancy. In this paper, we design a novel CNN structure, named SiPRNet, to recover a signal from a single Fourier intensity measurement. To effectively utilize the spectral information of the measurements, we propose a new Multi-Layer Perception block embedded with the dropout layer to extract the global representations. Two Up-sampling and Reconstruction blocks with self-attention are utilized to recover the signals from the extracted features. Extensive evaluations of the proposed model are performed using different testing datasets on both simulation and optical experimentation platforms. The results demonstrate that the proposed approach consistently outperforms other CNN-based and traditional optimization-based methods in single-shot maskless phase retrieval. The source codes of the proposed method have been released on Github: https://github.com/Qiustander/SiPRNet.