基于StarGAN和类别编码器的图像风格转换

图像风格转换技术已经融入到人们的生活中,并被广泛应用于图像艺术化、卡通化、图像着色、滤镜处理和去遮挡等实际场景中,因此,图像风格转换具有重要的研究意义与应用价值.StarGAN是近年来用于多域图像风格转换的生成对抗网络框架.StarGAN通过简单地下采样提取特征,然后通过上采样生成图片,但是生成图片的背景颜色信息、人物...

Image Style Transfering Based on StarGAN and Class Encoder

The image style transferring technology has been widely integrated into people''s life, and it is widely used in image artistry, cartoon, picture coloring, filter processing and occlusion removal of the practical scenes, so image style transferring has an important research significance and application value. StarGAN is a generative adversarial network framework for multi-domain image style transferring in recent years. StarGAN extracts features through simple down-sampling, and then generates images through up-sampling. However, the background color information and detailed features of people''s faces in the generated images are quite different from those in the input images. In this article, by improving the network structure of StarGAN, after analyzing the existing problems of the StarGAN, a UE-StarGAN model for image style transfering is proposed by introducing U-Net and edge-promoting adversarial loss function. At the same time, the class encoder is introduced into the generator of UE-StarGAN, and a small sample image style transferring model is designed to realize the small sample image style transfer. The results of this experiment show that the model can extract more detailed features, have some advantages in the case of small sample size, and to a certain extent, the qualitative and quantitative analysis results of the images can be improved after the image style transferring, which verifies the effectiveness of the proposed model.