Abstract
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.
Highlights
Advances in manufacturing lead to increasing demands on the quality and durability of technical components
This means that the input is not the LR image but an interpolated low-resolution image (ILR) which is the LR image interpolated to the dimensions of the higher resolution (HR) image using classic upsampling methods [4]
The results where compared to an upscaling of the LR image using only the nearest-neighbor, bilinear or bicubic algorithm
Summary
Advances in manufacturing lead to increasing demands on the quality and durability of technical components. The topography of a components surface such as the microstructure a have major impact on its performance. Surface metrology plays an important role in the research and development of technical components. Optical measurement methods have increasingly been used for surface metrology as they allow for a contactless and fast measurement of the topography. At the same time a large area should be measured in order to obtain reliable results. These two objectives are in conflict with each other, since lenses with a higher magnification and resolution usually have a smaller field of view. One approach to circumvent this is super-resolution imaging, where low-resolution (LR) images are upscaled to a higher resolution (HR) to obtain fine details whilst keeping a large field of view
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