Phase Extraction Neural Network (PhENN) with Coherent Modulation Imaging (CMI) for phase retrieval at low photon counts

• Main Authors: Kang, Iksung (Author), Zhang, Fucai (Author), Barbastathis, George (Author)
• Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor), Singapore-MIT Alliance in Research and Technology (SMART) (Contributor)
• Format: Article
• Language: English
• Published: The Optical Society / Optica Publishing Group, 2021-12-13T22:58:04Z.
• Subjects: Article
• Online Access: Get fulltext

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement. Imaging with low-dose light is of importance in various fields, especially when minimizing radiation-induced damage onto samples is desirable. The raw image captured at the detector plane is then predominantly a Poisson random process with Gaussian noise added due to the quantum nature of photo-electric conversion. Under such noisy conditions, highly ill-posed problems such as phase retrieval from raw intensity measurements become prone to strong artifacts in the reconstructions; a situation that deep neural networks (DNNs) have already been shown to be useful at improving. Here, we demonstrate that random phase modulation on the optical field, also known as coherent modulation imaging (CMI), in conjunction with the phase extraction neural network (PhENN) and a Gerchberg-Saxton-Fienup (GSF) approximant, further improves resilience to noise of the phase-from-intensity imaging problem. We offer design guidelines for implementing the CMI hardware with the proposed computational reconstruction scheme and quantify reconstruction improvement as function of photon count.
Southern University of Science and Technology (6941806)
Intelligence Advanced Research Projects Activity (FA8650-17-C-9113)
National Natural Science Foundation of China (11775105)