TFR: Texture Defect Detection with Fourier Transform using Normal
  Reconstructed Template of Simple Autoencoder
Jongwook Si Sungyoung Kim
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.