Semantic Segmentation-Guided Reconstruction and Artistic Style Synthesis of Intangible Cultural Heritage Patterns Using a Deep Learning Framework

The digital preservation and reconstruction of intangible cultural heritage (ICH) patterns have attracted increasing attention with the advancement of deep learning-based image synthesis techniques. Existing SegCycle-SPADE-based approaches have demonstrated potential for the structural segmentation and artistic reconstruction of traditional craft patterns; however, limitations remain, including limited dataset diversity, insufficient incorporation of cultural semantics, and inadequate preservation of structural pattern features during reconstruction. To address these challenges, this study proposes an improved SegCycle-SPADE framework for the semantic segmentation and artistic reconstruction of ICH pattern types. A Transformer-based segmentation model, SegFormer, is employed to accurately identify motif boundaries and pattern regions. In addition, a Cross-Attention Cultural Feature Fusion Module is introduced to enhance culturally significant motifs and improve feature representation. Pattern translation and reconstruction are performed using CycleGAN, while Spatially-Adaptive Denormalization (SPADE) is used for artistic style synthesis to maintain semantic consistency between segmentation maps and generated images. To improve model generalization and artistic diversity, the framework is trained using both the Miao Batik cultural motif dataset and the WikiArt dataset. Experimental results demonstrate that the proposed attention-guided SegCycle-SPADE framework improves segmentation accuracy and heritage-pattern reconstruction performance compared with existing generative adversarial network (GAN)-based reconstruction methods. The proposed framework provides a scalable solution for artificial intelligence-assisted cultural heritage documentation, reconstruction, and artistic revitalization of traditional pattern designs.