Despite its age the inspection of specular surfaces is still a topic of ongoing research. While sensory approaches to inspect such surfaces based on deflectometry are increasingly used in practice, the evaluation techniques using the acquired signals (images and reconstruction results) are often not sufficient. This work addresses the challenge of detecting defects with different characteristics on specular surfaces by using robust multiscale detection and classification. In order to process the signals obtained by deflectometry efficiently in all relevant scales, a method for generating an optimized biorthogonal wavelet filter bank with strong correlation to any number of anomaly classes is proposed. The filter bank is optimized for each defect class to obtain a sparse scale space representation. In addition a Bayesian classification approach is presented to classify defects like dents and pimples directly in the scale space.

Climate change is leading to a general shortage of raw water availability combined with more pronounced seasonality and dry phases. The goal of the collaborative research project TwinOptPRO is to contribute to EU-wide climate neutrality in 2050 by the minimization of energy supply for water treatment and pumps in drinking water distribution systems. For that purpose, a digital platform that combines different forecasting models with simulation and optimization modules was developed. The aim is to ensure secure and compliant supply to customers in the future while maximizing the use of renewable energy and minimizing costs.

In this paper we propose new models of two complementary optical sensors to obtain 2.5-D measurements of opaque surfaces: a deflectometric and a plenoptic sensor. The deflectometric sensor uses active triangulation and works best on specular surfaces, while the plenoptic sensor uses passive triangulation and works best on textured, diffusely reflecting surfaces. We propose models to describe the measurement uncertainties of the sensors for specularly to diffusely reflecting surfaces under consideration of typical disturbances like ambient light or vibration. The predicted measurement uncertainties of both sensors can be used to obtain optimized measurements uncertainties for varying surface properties on the basis of a combined sensor system. The models are validated exemplarily based on real measurements.