Texture is an essential information in image representation, capturing patterns and structures. As a result, texture plays a crucial role in the manufacturing industry and is extensively studied in the fields of computer vision and pattern recognition. However, real-world textures are susceptible to defects, which can degrade image quality and cause various issues. Therefore, there is a need for accurate and effective methods to detect texture defects. In this study, a simple autoencoder and Fourier transform are employed for texture defect detection. The proposed method combines Fourier transform analysis with the reconstructed template obtained from the simple autoencoder. Fourier transform is a powerful tool for analyzing the frequency domain of images and signals. Moreover, since texture defects often exhibit characteristic changes in specific frequency ranges, analyzing the frequency domain enables effective defect detection. The proposed method demonstrates effectiveness and accuracy in detecting texture defects. Experimental results are presented to evaluate its performance and compare it with existing approaches.

翻译：纹理是图像表达中的重要信息，能够捕捉图案与结构。因此，纹理在制造业中具有关键作用，并在计算机视觉与模式识别领域得到广泛研究。然而，实际纹理易受缺陷影响，这些缺陷会降低图像质量并引发各种问题。鉴于此，亟需精确有效的纹理缺陷检测方法。本研究采用简单自编码器与傅里叶变换进行纹理缺陷检测。所提方法将傅里叶变换分析与通过简单自编码器获取的重构模板相结合。傅里叶变换是分析图像与信号频域的强有力工具。此外，由于纹理缺陷通常在特定频率范围内表现出特征性变化，分析频域可实现有效的缺陷检测。该方法在纹理缺陷检测中展现出有效性与准确性。通过实验结果评估其性能，并与现有方法进行了对比分析。