Research on style transfer algorithm of full embroidery image based on semantic segmentation with convolutional neural network

This research focuses on exploring the challenges and problems faced by deep learning in image format alteration. Conventional neural networks often face problems such as blurred edge margins, lack of three-dimensionality in texture details of the generated stylized simulated images, and distorted lines in the stylization of simulated images. We have investigated the method of applying convolutional neural networks based on semantic segmentation for image style transformation with the aim of generating higher quality image data. We used neural network style transformation means combined with semantic segmentation techniques for FCN-CRF images to obtain the corresponding binary masks. The content corresponding masks were fed into the CNN for network analysis to achieve the image style transformation, which in turn generated the starting simulated image in full embroidery style. Finally, the image after the enhancement of the edge contours was integrated with the embroidered fabric and the initial simulated image, so we obtained a more superior visual perception. Using the optimization technique, we can enhance the hierarchical structure of the foreground and background images in the style simulation effect image, and we can improve the edge definition and clarity of the style simulation effect image, so as to create a superior style simulation visual performance than the traditional algorithm.