关键词: 图像风格迁移, 残差网络, VGG网络

Abstract:

Image style migration technology refers to the conversion of an image into a similar image to a famous painting style through learning （using a convolutional neural network）. The NAAS proposed by Gatys uses the VGG network to design a loss network, and the style migration image is obtained through repeated iterations. Li Feifei has introduced the residual network on the basis of NAAS, and the calculation is accelerated by using the fast connection feature of the residual element. This paper proposes improvements for the following two aspects：the classical residual element structure is adjusted, and the standard convolution is converted into point convolution and deep convolution, which reduces the amount of calculation while ensuring the convolution effect. The loss network is simplified. The fourth and fifth layers in the model are highly consistent in structure, and the style restoration of the two layers is basically the same as the content reconstruction effect. Therefore, the fifth layer is deleted and the structural parameters are adjusted accordingly, and the redundant parameters are removed. The effect of style restoration and content reconstruction is guaranteed while reducing the amount of parameters.

Key words: image style transformation, residual network, VGG network