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. 2018 Sep 25;8:14351. doi: 10.1038/s41598-018-32713-7

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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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Details of spatial denoising results for HD data with Fourier transform (FT) and Deep Neural Networks (DNN) shown in details as they performed relatively the best (all images are given in Supplementary Materials S10). Zoom-ins on tissue structure and edges are shown to better highlight noise rejection and potential artifacts introduced by a given method.