Super-resolution for 2.5D height data of microstructured surfaces using the vdsr network
Stefan Siemens, Markus Kästner, Eduard Reithmeier · EPJ Web of Conferences · 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.