Research on ink style image generation based on deep learning

Tianyi Zheng; Zhangqin Huang; Runmin Zhang

doi:10.1117/12.2682550

26 May 2023 Research on ink style image generation based on deep learning

Tianyi Zheng, Zhangqin Huang, Runmin Zhang

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 127001U (2023) https://doi.org/10.1117/12.2682550
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

Most of the current existing style transfer methods are based on photographs or Western paintings. Due to the inherent differences between Chinese and Western paintings, direct application of existing algorithms cannot generate satisfactory style transfer results for Chinese ink paintings. Accordingly, this paper proposes a new feedforward style transfer method, called inkStyle, which uses a draw Network to transfer global style patterns at low resolution and a higher resolution details networks to modify local style patterns in a pyramidal manner based on the multi-level Laplacian filtering output of the content image. The experimental results show that the method in the paper performs better and produces better visual results.

Citation Download Citation

Tianyi Zheng, Zhangqin Huang, and Runmin Zhang "Research on ink style image generation based on deep learning", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 127001U (26 May 2023); https://doi.org/10.1117/12.2682550

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