Improved generative adversarial networks for denoising fabric defect images

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In order to solve the problems of noise, blurring, and loss of detail in fabric defect images due to camera location and factory environments, a fabric defect image denoising method based on improved generative adversarial network (GAN) is proposed. A mathematical model is developed to simulate the noisy images of fabric blemishes in a real factory environment. GAN, as a deep learning method widely used in the field of computer vision, generates high-quality data samples through the competition between generators and discriminators. In this study, the traditional GAN is improved by introducing multiple loss terms and adopting a new adaptive weighting strategy in order to make the denoised image closer to the original clear image while better preserving the structural and detailed features of the image. In addition, the network structure is improved by introducing a multiscale discriminator, a convolutional block attention module (CBAM) and a residual network to capture feature information at different spatial scales more effectively and to promote the propagation of low-level features. The experimental results show that the method can effectively remove fabric defective image noise, and its subjective effect and objective indexes are better than similar algorithms. The research results are of great value to quality inspectors, related researchers, and image denoising technology professionals in the textile industry. These results help to improve the efficiency and accuracy of fabric defect detection and image denoising processing, thus promoting the development of related technologies.