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Image neural style transfer with global and local optimization fusion

Zhao, Hui-Huang, Rosin, Paul L., Lai, Yu-Kun, Lin, Mu-Gang and Liu, Qin-Yun 2019. Image neural style transfer with global and local optimization fusion. IEEE Access 7 (1) , pp. 85573-85580. 10.1109/ACCESS.2019.2922554

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Abstract

This paper presents a new image synthesis method for image style transfer. For some common methods, the textures and colors in the style image are sometimes applied inappropriately to the content image, which generates artifacts. In order to improve the results, we propose a novel method based on a new strategy that combines both local and global style losses. On the one hand, a style loss function based on a local approach is used to keep the style details. On the other hand, another style loss function based on global measures is used to capture the more global structural information. Results on various images show that the proposed method reduces artifacts while faithfully transferring the style image’s characteristics and preserving the structure and color of the content image.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 2169-3536
Date of First Compliant Deposit: 17 June 2019
Date of Acceptance: 3 June 2019
Last Modified: 19 Oct 2019 03:30
URI: http://orca.cf.ac.uk/id/eprint/123503

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