Abstract

By exploiting the total variation (TV) regularization scheme and the contrast transfer function (CTF), a phase map can be retrieved from single-distance coherent diffraction images via the sparsity of the investigated object. However, the CTF-TV phase retrieval algorithm often struggles in the presence of strong noise, since it is based on the traditional compressive sensing optimization problem. Here, convolutional neural networks, a powerful tool from machine learning, are used to regularize the CTF-based phase retrieval problems and improve the recovery performance. This proposed method, the CTF-Deep phase retrieval algorithm, was tested both via simulations and experiments. The results show that it is robust to noise and fast enough for high-resolution applications, such as in optical, x-ray, or terahertz imaging.

© 2019 Optical Society of America

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Corrections

Chen Bai, Meiling Zhou, Junwei Min, Shipei Dang, Xianghua Yu, Peng Zhang, Tong Peng, and Baoli Yao, "Robust contrast-transfer-function phase retrieval via flexible deep learning networks: publisher’s note," Opt. Lett. 44, 5561-5561 (2019)
https://www.osapublishing.org/ol/abstract.cfm?uri=ol-44-22-5561

17 October 2019: A typographical correction was made to the author listing.


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