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

TL;DR

SiSPRNet is a novel deep neural network designed for single-shot phase retrieval from Fourier intensity measurements, utilizing spectral feature extraction and self-attention to improve reconstruction quality and robustness.

Contribution

The paper introduces SiSPRNet, a new end-to-end deep learning architecture that effectively leverages spectral information and global correlations for phase retrieval from a single measurement.

Findings

High-quality phase reconstruction demonstrated on various datasets.

Effective noise reduction through MLP feature extraction and dropout.

Robust performance in practical optical experiments.

Abstract

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.