Super Resolution Image Reconstruction of Textile Based on SRGAN
Junchao Li, Liming Wu, Shiman Wang, Wenhao Wu, Feiyang Song, Gengzhe Zheng · 2019
For the problem of image distortion in textile flaw detection, a super-resolution image reconstruction technique based on GAN (Generative adversarial network) can reconstruct the obtained low-pixel image into a high-pixel image. The generative adversarial network consists of a discriminative network and a generative network. Generative network is responsible for generate high-resolution images, discriminative network is responsible for identifying the authenticity of the image. the generative loss and discriminative loss continuously optimize the network and guide the generation of high-quality images. The experimental results show that, the PNSR of SRGAN is 0.83 higher than that of the Bilinear, and the SSIM is higher than 0.0819. SRGAN can get a clearer image and reconstruct a richer texture, more high-frequency details, and easier to identify defects, which is important in the flaw detection of fabrics.