Super-resolution for 2.5D height data of microstructured surfaces using the vdsr network

Stefan Siemens, Markus Kästner, Eduard Reithmeier, O. Frazao, M.F. Costa, H. Michinel
2020 EPJ Web of Conferences  
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.
doi:10.1051/epjconf/202023806014 fatcat:lux4xldpj5b77plskeqmhpfjgy