Image style transfer based on improved convolutional neural

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Abstract
Image style transfer is a hot issue in the field of computer vision. It refers to the process that an image (content image) has a specified artistic style (style image) through the algorithm, which has an important application value. The complex spatial structure in the process of style transfer will make the details blurred and the local structure of the image distorted. In this paper, a new feature detection network is introduced for feature extraction of style content and style content, which has fewer simple parameters. In the transformation network, the adaptive Instance standardization layer is added after the convolution layer to improve the retention ability of the spatial structure of the content image. Large convolution kernels are replaced to reduce the number of model parameters while maintaining the same receptive field. The network model built in this paper can realize the rapid migration of various styles, strengthen the structural characteristics of images, and improve the detail effect of stylized images significantly.
Funding Information
  • Key Research and Development Program of Shanxi Province (2020GY-152)