Optical processing for the detection of faults in interferometric patterns

The detection and classification of faults is a major task for optical nondestructive testing in industrial quality control. Interferometric fringes, obtained by real-time optical measurement methods, contain a large amount of image data with information about possible defect features. This mass of data must be reduced for further evaluation. One possible way is the filtering of these images applying the adaptive wavelet transform. The wavelet transform has been proved to be a capable tool in the detection of structures with definite spatial resolution. In this paper it is shown the extraction and classification of disturbances in interferometric fringe patterns, the application of several wavelet functions with different parameters for the detection of faults, and the combination of wavelet filters for fault classification. Furthermore the implementation of complex valued wavelet filters and correlation filters is shown. We will present an algorithm to classify interferometric fringe patterns. In order to achieve real-time processing a hybrid opto-electronic system with a digital image processing and an optical correlation module is favored. The calculated wavelet filters are implemented into the optical correlator system that is based on liquid-crystal spatial light modulators. So, all discussed items were verified experimentally in the optical setup