关键词: 深度卷积神经网络, 图像风格迁移, 语义分割, Matting算法

Abstract: Aiming at the problem of image transfer and loss of content details in image style transfer, an image style transfer algorithm with semantic segmentation based on deep convolutional neural network is proposed. Firstly, the content image loss and the style image loss function are defined. Then the content image and the style image are separately semantically segmented, and the Matting algorithm is applied to the content image, and the least squares penalty function is used to enhance the image edge authenticity. The content reconstruction and style reconstruction generate new images. It analyzes and compares the images generated by the Neural Style modified method, CNNMRF method and the image style transfer method with semantic segmentation. The experimental results and quality evaluation show that 70% of the images generated by the image style transfer method with semantic segmentation have no obvious image distortion and the content details are intact. Therefore, this method can solve the problem of image distortion and detail loss, so that the rich image can be accurately styled.

Key words: deep convolutional neural network, image style transfer, semantic segmentation, Matting algorithm