Hu Guang-Hua, Wang Qing-Hui, Zhang Guo-Hui
Appl Opt. 2015 Apr 1;54(10):2963-80. doi: 10.1364/AO.54.002963.
An unsupervised approach for the inspection of defects in textiles by applying Fourier analysis and wavelet shrinkage is proposed. It does not rely on any reference images. For each sample under inspection, the periodic pattern in the background is first eliminated by zero-masking their dominant frequency components that show high gradient values in the spectrum. The Fourier-restored residual image is then denoised by wavelet shrinkage. The approximation coefficients and the processed wavelet coefficients are individually back-transformed to produce a pair of reconstructions from which either the low or the high-frequency information about the defects can be segmented using a simple thresholding process. The performance of the method has been extensively evaluated by a wide variety of samples with different defect types and texture backgrounds. The effectiveness of the proposed method is demonstrated by the experimental results in comparison with other methods.
提出了一种通过应用傅里叶分析和小波收缩来检测纺织品缺陷的无监督方法。它不依赖于任何参考图像。对于每个被检测的样本，首先通过将其在频谱中显示高梯度值的主导频率分量进行零掩蔽来消除背景中的周期性图案。然后通过小波收缩对傅里叶恢复后的残差图像进行去噪。将近似系数和处理后的小波系数分别进行逆变换，以生成一对重建图像，通过简单的阈值处理可以从其中分割出关于缺陷的低频或高频信息。该方法的性能已通过具有不同缺陷类型和纹理背景的各种样本进行了广泛评估。与其他方法相比，实验结果证明了所提方法的有效性。
