Automatic band selection for wavelet reconstruction in the application of defect detection

In this paper, we present a multiresolution approach for the inspection of local defects embedded in homogeneously textured surfaces. It is based on an efficient image restoration scheme using the wavelet transforms. By properly selecting the smooth subimage or the combination of detail subimages at different resolution levels for image reconstruction, the global repetitive texture pattern can be effectively removed and only local anomalies are preserved in the restored image. A wavelet band selection procedure is developed to automatically determine the best reconstruction parameters based on the energy distribution of wavelet coefficients. Experimental results show that the decomposed subimages and the number of resolution levels determined by the automatic band selection scheme are similar to the manual selection results, and the defects in a variety of real textures including machined surfaces, natural wood, sandpaper and textile fabrics are well detected.