JOURNAL OF TEXTILE RESEARCH ›› 2018, Vol. 39 ›› Issue (01): 157-163.doi: 10.13475/j.fzxb.20170404407

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Sparse representation of woven fabric texture based on discrete cosine transform over-complete dictionary

  

  • Received:2017-04-20 Revised:2017-08-15 Online:2018-01-15 Published:2018-01-16

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Abstract:

In order to investigate the stationary and comparability of the algorithm for woven fabric texture representation based on dictionary learning, the sparse representation with over-complete discrete cosine transform (DCT) dictionary was used to characterize the woven fabric texture. Firstly, the influence of sparsity on woven fabric texture reconstruction was investigated. Ttwo indexes with root mean square error and peak signal to noise ratio were calculated to quantify the approximation of original image and reconstructed image. And then the final chosen sparsity value is 10, the image patch size is 8 pixel × 8 pixel, and the number of dictionary atom is 256. Experiments demonstrated that the proposed algorithm is quick, has simple calculation and can achieve rather good effect. In addition, the method not only can achieve stable results, but also its peak signal to noise ratio is better about 4dB than pincipal compinent analysis and non-sparse representation algorithm on average, which is only inferior to the K singular value decomposition learned dictionary.

Key words: woven fabric texture, discrete cosine transform over-complete dictionary, sparse representation, pincipal component analysis

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[2] . Fabric defect detection algorithm research based on sparse optimization [J]. Journal of Textile Research, 2016, 37(05): 56-61.
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