TY - JOUR
KW - Applied research
KW - article
KW - Artificial intelligence
KW - Color
KW - controlled study
KW - Deep learning
KW - Feature extraction
KW - Generative adversarial network
KW - generator
KW - human
KW - Inheritance
AU - Jialiang He
AU - Hong Tao
AB - In light of the challenges currently facing the inheritance of blue calico, including the reduction in the number of inheritors and the contraction of the market, this paper puts forth a stylistic transfer method based on an enhanced cycle consistency generative adversarial network. This approach is designed to facilitate the creation of novel designs for traditional blue calico patterns. To address the shortcomings of existing style transfer models, including the generation of blurry details, poor texture and color effects, and excessive model parameters, we propose the incorporation of the Ghost convolution module and the SRM attention module into the generator network structure. This approach aims to reduce the model parameter quantity and computational cost while enhancing the feature extraction ability of the network. The experimental results demonstrate that the method proposed in this paper not only effectively enhances the content details, texture, and color effects of the generated images, but also successfully combines traditional blue calico with modern daily necessities, thereby enhancing its appeal to young people. This research provides novel insights into the digital protection and innovative development of traditional culture, and illustrates the extensive potential applications of deep learning technology in the field of cultural heritage. © The Author(s) 2025.
BT - Scientific Reports
DO - 10.1038/s41598-025-96587-2
M1 - 1
N1 - Type: Article
N2 - In light of the challenges currently facing the inheritance of blue calico, including the reduction in the number of inheritors and the contraction of the market, this paper puts forth a stylistic transfer method based on an enhanced cycle consistency generative adversarial network. This approach is designed to facilitate the creation of novel designs for traditional blue calico patterns. To address the shortcomings of existing style transfer models, including the generation of blurry details, poor texture and color effects, and excessive model parameters, we propose the incorporation of the Ghost convolution module and the SRM attention module into the generator network structure. This approach aims to reduce the model parameter quantity and computational cost while enhancing the feature extraction ability of the network. The experimental results demonstrate that the method proposed in this paper not only effectively enhances the content details, texture, and color effects of the generated images, but also successfully combines traditional blue calico with modern daily necessities, thereby enhancing its appeal to young people. This research provides novel insights into the digital protection and innovative development of traditional culture, and illustrates the extensive potential applications of deep learning technology in the field of cultural heritage. © The Author(s) 2025.
PY - 2025
T2 - Scientific Reports
TI - Applied research on innovation and development of blue calico of Chinese intangible cultural heritage based on artificial intelligence
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003222118&doi=10.1038%2fs41598-025-96587-2&partnerID=40&md5=656f450d4eb53f60a69eddb63c537824
VL - 15
ER -