Abstract
In this paper, we analyze a machine-learning-based non-iterative phase retrieval method. Phase retrieval and its applications have been attractive research topics in optics and photonics, for example, in biomedical imaging, astronomical imaging, and so on. Most conventional phase retrieval methods have used iterative processes to recover phase information; however, the calculation speed and convergence with these methods are serious issues in real-time monitoring applications. Machine-learning-based methods are promising for addressing these issues. Here, we numerically compare conventional methods and a machine-learning-based method in which a convolutional neural network is employed. Simulations with several conditions show that the machine-learning-based method realizes fast and robust phase recovery compared with the conventional methods. We also numerically demonstrate machine-learning-based phase retrieval from noisy measurements with a noisy training data set for improving the noise robustness. The machine-learning-based approach used in this study may increase the impact of phase retrieval, which is useful in various fields, where phase retrieval has been used as a fundamental tool.
Highlights
Optical phenomena are described using waves in wave optics [1]
The error reduction (ER) method and the hybrid input-output (HIO) method are employed as the baseline of the conventional iterative phase retrieval, as shown in Fig. 3 [14]
The phase retrievals with the two networks when using the noisy and noiseless training data sets and the ER and HIO methods were compared under different measurement noise levels, namely, signal-to-noise ratios (SNRs) of 10, 20, 30 and ∞ dB
Summary
Optical phenomena are described using waves in wave optics [1]. image sensors detect only the intensity of a light wave and disregard its phase. Phase retrieval algorithms basically employ an iterative process between the object and sensor domains to recover the phase from the intensity, and they require many iterations to achieve sufficient convergence [14, 15] Machine learning, such as deep learning, has recently been used for robust and fast phase retrieval. Optical Review (2020) 27:136–141 media, computer-generated holograms, wavefront sensing, and pulse measurement [27,28,29,30,31,32,33,34] Such DNN-based inversion has been introduced to optical sensing methods other than phase retrieval [35,36,37,38]. The fast, noise-robust phase retrieval based on machine learning demonstrated in this paper will contribute to various fields, including biomedicine, security, and astronomy
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