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. 2018 Nov 8;8:16520. doi: 10.1038/s41598-018-34525-1

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Examples of the performance of CDI NN in testing. (A) Input diffraction intensities to CDI NN. (B) Actual shape of the corresponding object and (C) The shape predicted by sCDI NN. (D) The actual phase structure of the object and (E), the phase structure predicted by pCDI NN. CDI NN successfully recovers structure and shape for a variety of different shapes and phase structures. (F,G) Show the structure and phase obtained after running iterative phase retrieval. Phase wrap artifacts are seen in a few of the recovered phases. Intensity units are dimensionless while phase is in radians.