Super-resolution for 2.5D height data of microstructured surfaces using the vdsr network
Leibniz University Hannover, Institute of Measurement and Automatic Control, Nienburger Straße 17, 30167 Hannover, Germany
* Corresponding author: firstname.lastname@example.org
Published online: 20 August 2020
In this work super-resolution imaging is used to enhance 2.5D height data of thermal sprayed Al2O3 ceramics with stochastically microstructured surfaces. The data is obtained by means of a confocal laser scanning microscope. By implementing and training a Very Deep Super-Resolution neural network to generate residual images an improvement of the peak signal-to-noise ratio and structural similarity index can be observed when compared to classic interpolation methods.
© The Authors, published by EDP Sciences, 2020
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