READ ME File For 'Dataset of the paper: 2D Super-resolution Metrology Based on Superoscillatory Light'

Dataset DOI: 10.5258/SOTON/D3175

Date that the file was created: July, 2024

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GENERAL INFORMATION
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ReadMe Author: Yu Wang, University of Southampton [https://orcid.org/0000-0002-0636-5102]

Date of data collection: February, 2024

Information about the geographic location of data collection: University of Southampton, Building 53, 2029

Related projects:

This work is supported by the UK Engineering and Physical Science Research Council (grants EP/T02643X/1, EP/X03495X/1), the Royal Society (project number RG\R2\232531), the Singapore National Research Foundation (grant No. NRF-CRP23-2019-0006), the Singapore Ministry of Education (grant MOE2016-T3-1-006), and the China Scholarship Council (202006150050; Y. Wang).

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SHARING/ACCESS INFORMATION
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Recommended citation for the data: https://doi.org/10.5258/SOTON/D3175

This dataset supports the publication:

AUTHORS: Yu Wang, Eng Aik Chan, Carolina Rendón-Barraza, Yijie Shen, Eric Plum, Jun-Yu Ou
TITLE: 2D Super-resolution Metrology Based on Superoscillatory Light
JOURNAL: Advanced Science


Licence CC-BY


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DATA & FILE OVERVIEW
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This dataset contains: 3 Excel files including all initial data for plotting Figures 2-4.
Fig. 2 data.xlsx contains one sheet, where Columns A and B are the data for plotting Fig.2 (b) and Columns D and E are the data for plotting Fig.2 (c).
Fig. 3 data.xlsx contains one sheet, where Columns A and B are the data for plotting Fig.3 (b) and Columns D and E are the data for plotting Fig.3 (c).
Fig. 4 data.xlsx contains one sheet, where Columns A, B, C, and D are the data for plotting Fig.4 (e) and Columns F, G, H, and I are the data for plotting Fig.4 (f).



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METHODOLOGICAL INFORMATION
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Fig.2 data were generated from the neural network retrieved values VS. the true widths (or lengths) of ellipses through traning experimental images.
Fig.3 data were generated from the neural network retrieved values VS. the true widths (or lengths) of ellipses through traning simulated images.
Fig.4 data were generated from the neural network prediction minus the truth under four different illuminations.