@Article{JFBI-16-1,
author = {Zhang, Jing and Yan, Jiang},
title = {Style Transfer Technology of Batik Pattern Based on Deep Learning},
journal = {Journal of Fiber Bioengineering and Informatics},
year = {2023},
volume = {16},
number = {1},
pages = {57--67},
abstract = {<p style="text-align: justify;">AI painting has recently come into public view, improving the efficiency of users&#39; creations. At present,
the research and application of popular products such as characters and landscapes are more, but the
research of Miao batik patterns is lacking. Therefore, this paper studies the style transfer of batik
patterns from two aspects. First, a local style transfer model of batik patterns with enhanced edges is
proposed. The loss function is composed of local content loss, local style loss and Laplacian loss, and
the generated images have good performance in detail texture and color space. The other is to use the
existing model in the AI painting tool Stable Diffusion for style transfer of batik patterns. It performs
well in running time and memory occupation, but the generated image cannot inherit the style and
content images well in color and detail.</p>},
issn = {2617-8699},
doi = {https://doi.org/10.3993/jfbim02171},
url = {https://global-sci.com/article/86353/style-transfer-technology-of-batik-pattern-based-on-deep-learning}
}