An image style transfer model based on convolutional neural network (CNN)

With the rise of deep learning, the cross collision between artificial intelligence and art, represented by image style transfer, has attracted high attention in the fields of graphic and image technology and art. Based on Convolutional Neural Networks(CNN), this article discusses an image style transfer model. Firstly, define the content loss function and style loss function ,weigh them to calculate the total loss function, incorporate texture features extracted by the Canny operator into the content image; secondly, use the cost function to calculate the values representing content and style for comparison between the initial style and the target style, and then generate a new target image through deformation and combination; finally, train the VGG model to generate the output of the content and style layers of the image, and calculated the corresponding layer parameters applied to the original image for retraining to achieve the minimum loss value. The verification results indicate that adding the Canny operator makes the edges of the style transfer graph more delicate and the contours clearer. This model has a very wide range of applications, such as video processing, photo beautification, social communication, etc. It is currently one of the popular technologies in artificial intelligence.