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. 2020 Oct 22;10:18093. doi: 10.1038/s41598-020-75172-9

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© The Author(s) 2020

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Performance of 2D CARE image restoration. Trained model is applied to images acquired by accumulating 15 consecutive frames of 0.27 × 0.27 mm² (a) and 0.9 × 0.9 mm² (c) image sequences (both 1 MPx, 2 s acquisition time). Restored images (b,d) display decreased noise levels and higher contrast for both fields of view. (e,f) line profiles through adjacent keratinocytes corresponding to (a,b) and (c,d) respectively (yellow lines). The line intensity profiles in the network prediction images (red) show enhanced definition of cell nuclei (black arrows) compared to the low cellular contrast to background in the raw images (blue). The insets in (g) and (h) are close-ups of the blue and red areas outlined in (c) and (d), respectively.