Inspection of specular surfaces using optimized M-channel wavelets

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.