Using Image Style Transfer Technology Based on U-Net Architecture to Improve the Information Expression Effect of Intangible Cultural Heritage Brand Visual Posters
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Abstract
Traditional poster design often struggles to efficiently and accurately preserve the complex styles, textures, and cultural connotations of intangible cultural heritage (ICH), resulting in limited visual information expression. To address this challenge, this paper proposes an adaptive feature fusion style transfer model based on the U-Net architecture, which provides a high-fidelity visual information processing framework with potential methodological implications for intelligent image transmission and multi-scale feature representation in advanced engineering systems. The proposed model exploits the symmetric encoder–decoder structure and skip connections of U-Net to achieve cross-scale extraction and transfer of ICH style features while preserving fine content details. An Adaptive Instance Normalization (AdaIN) module is employed to decouple and fuse style and content features in the latent space, enabling efficient style injection without sacrificing structural consistency. Skip connections further facilitate the integration of high-resolution semantic information with stylized representations, producing visual posters that simultaneously exhibit cultural authenticity and clear information delivery. Experimental results demonstrate superior performance, achieving a Style Consistency Index (SCI) of 0.28, an Information Clarity Metric (ICM) of 0.95, and a Brand Recognition Perception Score (BRPS) of 4.50, outperforming existing comparison methods. The proposed approach effectively improves both artistic quality and information expression efficiency and provides a reliable feature fusion strategy for intelligent visual communication and digital information processing applications.
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