Improved Image Style Transfer Based on VGG-16 Convolutional Neural Network Model. Issue 1 (1st January 2023)

• Record Type: Journal Article
• Authors: Shu, Chenhao; Hu, Xichuan
• Is Part Of: Journal of physics. Volume 2424:Issue 1(2023)
• Publication Date: 2023-01-01
• Subjects: Image style transfer -- VGG-16 convolutional neural network model -- TensorFlow -- Multi-style image; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2424/1/012021
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In the field of computer vision, image style transfer is an important research direction. With the promotion of artificial intelligence, this technology is becoming more and more popular. Compared with the current image style transfer technology based on artificial intelligence, the traditional technology appears to be complex, time-consuming and inefficient. Migration technology is mainly based on images of the current mainstream style VGG - 16 convolution neural network model, adopts TensorFlow framework, in this article to a picture before the smart migration style photo style, texture features such as the methods of improvement, by modifying the style loss function, makes the image content images can transfer a variety of style, Finally, it is verified by relevant experiments.