A noise filtration technique for fabric defects image using curvelet transform domain filters

A noise filtration technique for fabric defects image using curvelet transform domain Filters is proposed in this paper. Firstly, we used FDCT_WARPING to decompose image into five scales curvelet coefficients. Secondly, the proposed algorithm distinguished major edges from noise background at the third scale. Thirdly, the possible lost edges in the procedure above were detected according to the decaying lever of the coefficients. Fourthly, the edges of the defect at the second scale were detected by four correlation coefficients in the two directions at the third scale. Fifthly, the curvelet coefficients at the fourth scale are filtered by the decaying lever. Sixthly, the curvelet coefficients at the fifth scale are filtered by hard threshing. Finally, the processed coefficients are reconstructed. The tests on the developed algorithms were performed with images from TILDA's Textile Texture Database, and suggest that the new approach outperforms wavelet methods in image denoising.