An Improved Image Style Transfer Algorithm Based on Deep Learning Network

Abstract

Aiming at the problem of local style migration distortion in image stylization, an improved image style migration algorithm based on deep learning network is proposed. Firstly, the VGGNet-19 network is used to extract the convolution layer features of images. Then the characteristics of convolution layer are analyzed, and the combination of style and content features is studied. The Block3 layer with the smallest content loss and style loss is selected for feature fusion with conv1_1, conv2_1, conv3_1, conv4_1 and conv5_1 layers. Finally, under the optimal combination of features, Adam algorithm is used to optimize the image style migration. The experimental results show that the proposed algorithm can effectively improve the distortion of local style migration and provide theoretical support for the implementation of style migration technology.