CNN-Based Embroidery Style Rendering

Nonphotorealistic rendering (NPR) techniques are used to transform real-world images into high-quality aesthetic styles automatically. NPR mainly focuses on transfer hand-painted styles to other content images, and simulates pencil drawing, watercolor painting, sketch painting, Chinese monochromes, calligraphy and, so on. However, digital simulation of Chinese embroidery style has not attracted researcher’s much attention. This study proposes an embroidery style transfer method from a 2D image on the basis of a convolutional neural network (CNN) and evaluates the relevant rendering features. The primary novelty of the rendering technique is that the strokes and needle textures are produced by the CNN and the results can display embroidery styles. The proposed method can not only embody delicate strokes and needle textures but also realize stereoscopic effects to achieve real embroidery features. First, using conditional random fields (CRF), the algorithm segments the target content and the embroidery style images through a semantic segmentation network. Then, the binary mask image is generated to guide the embroidery style transfer for different regions. Next, CNN is used to extract the strokes and texture features from the real embroidery images, and transfer these features to the content images. Finally, the simulating image is generated to show the features of the real embroidery styles. To demonstrate the performance of the proposed method, the simulations are compared with real embroidery artwork and other methods. In addition, the quality evaluation method is used to evaluate the quality of the results. In all the cases, the proposed method is found to achieve needle visual quality of the embroidery styles, thereby laying a foundation for the research and preservation of embroidery works.