Real-time image style transformation based on deep learning

Xianlin Zhang; Yixin Luan; Xueming Li

doi:10.1117/1.JEI.27.4.043045

14 August 2018 Real-time image style transformation based on deep learning

Xianlin Zhang, Yixin Luan, Xueming Li

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 4, 043045 (August 2018). https://doi.org/10.1117/1.JEI.27.4.043045

Abstract

Image style transfer processing is an important part of image processing, which processes image information such as the color, silhouette, and line to other image styles by computer. It has made great progress in recent years, with the development of deep learning. This paper designs a deep architecture to generate highly stylized images based on convolutional neural networks and generative adversarial networks (GANs). In addition, we construct an image style similarity measure model, which can discriminate whether the generated style image is similar to the real ones. Experimental results show that the generated style images can achieve good visual results compared with related image style transformation algorithms. Generally, we propose an efficient real-time image style transfer model to generate highly stylized images.

Citation Download Citation

Xianlin Zhang, Yixin Luan, and Xueming Li "Real-time image style transformation based on deep learning," Journal of Electronic Imaging 27(4), 043045 (14 August 2018). https://doi.org/10.1117/1.JEI.27.4.043045

Received: 21 March 2018; Accepted: 1 August 2018; Published: 14 August 2018

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