Abstract
Image style transfer aims to obtain content images with corresponding styles by migrating style information to content images, but transfer models in recent years have certain application limitations, for which good image quality and transfer speed of the model cannot be guaranteed at the same time, and it’s common that clear edges cannot be maintained when stylizing. This paper proposes a new feed-forward model Haar Based Network (HaarStyle), which uses different resolution modules to complement the edge information and content features of the image to improve its quality. It is experimentally demonstrated that HaarStyle can ensure a certain transmission speed with fewer artifacts, avoiding the problem of over stylization.
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Chen, H., Li, M., Lei, Y. (2023). HaarStyle:Revision Style Transfer Based on Multiple Resolutions. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14254. Springer, Cham. https://doi.org/10.1007/978-3-031-44207-0_21
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DOI: https://doi.org/10.1007/978-3-031-44207-0_21
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