Abstract:

In order to wel adapt the woven fabric texture and reduce the algorithm running time, three basic weave patterns (plain, twill and satin) were chosen as trained samples to learn an adaptive dictionary by K-means singular value decomposition (K-SVD) dictionary learning approach. In order to select appropriate sparsity cardinality T for different applications, peak sognal to noise ratio (PSNR) and structural similarity index measurement  (SSIM) wer chosen as evaluating preformance indexes. For regular fabric texture image reconstruction, T=6, the experimental results demonstrate that the proposed method not only can approximate fabric samples well, but also can improve the quality of reconsturcted image (in terms of PSNR and SSIM), in comparison with discrete cosine transformation dectionary. In addition, for fabric flaw detection, T=4, the K-SVD can well adapt samples with defects, and has stronger capability of identifying defects, compared with discrete cosine transformation dictionary.

Key words: woven fabric texture characterization, discrete cosine transformation dictionary, K-SVD dictionary, edfect detection, image reconstruction