  With the success of deep learning methods in many image processing tasks,
deep learning approaches have also been introduced to the phase retrieval
problem recently. These approaches are different from the traditional iterative
optimization methods in that they usually require only one intensity
measurement and can reconstruct phase images in real-time. However, because of
tremendous domain discrepancy, the quality of the reconstructed images given by
these approaches still has much room to improve to meet the general application
requirements. In this paper, we design a novel deep neural network structure
named SiSPRNet for phase retrieval based on a single Fourier intensity
measurement. To effectively utilize the spectral information of the
measurements, we propose a new feature extraction unit using the Multi-Layer
Perceptron (MLP) as the front end. It allows all pixels of the input intensity
image to be considered together for exploring their global representation. The
size of the MLP is carefully designed to facilitate the extraction of the
representative features while reducing noises and outliers. A dropout layer is
also equipped to mitigate the possible overfitting problem in training the MLP.
To promote the global correlation in the reconstructed images, a self-attention
mechanism is introduced to the Up-sampling and Reconstruction (UR) blocks of
the proposed SiSPRNet. These UR blocks are inserted into a residual learning
structure to prevent the weak information flow and vanishing gradient problems
due to their complex layer structure. Extensive evaluations of the proposed
model are performed using different testing datasets of phase-only images and
images with linearly related magnitude and phase. Experiments were conducted on
an optical experimentation platform to understand the performance of different
deep learning methods when working in a practical environment.
