https://doi.org/10.1051/epjconf/202023806014
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: stefan.siemens@imr.uni-hannover.de
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
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
