A novel surface defect detection method based on non-subsampled shearlet transform (NSST) was proposed for detecting defects with random noise,low contrast,uneven background and regularly distributed grinding texture in magnetic tile surface images.The method first employed NSST to decompose the original magnetic tile defect image into one low frequency subband and a series of high frequency subbands with multiple frequency domains and shearing directions.Then,the defect signals were processed pertinently and separately according to the varied characteristics of defects in the low and high frequency domains.In the low frequency subband,the mean images of row and column were computed,and an adaptive threshold surface constructed by extending the column mean image along the row mean image was adopted to remove the uneven background.Meanwhile,as weak defect signals and significant defect signals exhibited large variance,respectively,and energy at the same decomposition scale in high frequency domains and both of them were small in noise and background interference pixels,a discriminator based on the variance and energy of high frequency Shearlet coefficients was proposed for the removal of noise and background interference in the high frequency subbands.Finally,a denoising image with high contrast and even background could be reconstructed by applying inverse NSST to the modified Shearlet coefficients,and the defects could be accurately extracted using an adaptive threshold segmentation method.Experimental results showed that the false positive rate,the false negative rate and the accuracy rate of the proposed method are 8.8%,5.0% and 93.1%,respectively,and the average elapsed time is 0.629 s with the software of MATLAB.Moreover,as the proposed method is effective in eliminating the interferences of uneven background,grinding texture and random noise,it has obvious superiorities over the existing ones in terms of both accuracy and robustness.
