融合多尺度特征的敦煌壁画风格迁移

Style transfer of Dunhuang murals fusing multi-scale features

关键词:  深度学习  风格迁移  生成对抗网络  多尺度特征  敦煌壁画

Key words:  deep learning  style transfer  generative adversarial network  multi-scale features  Dunhuang murals

摘要:

为解决现有风格迁移技术在处理敦煌壁画时遇到的因高饱和度矿物颜料、精细纹理及层次结构复杂导致的色彩失真、细节模糊、层次结构失调问题,本文提出多尺度敦煌风格迁移网络,改进循环生成对抗网络来实现高质量敦煌壁画风格迁移。通过引入自适应局部膨胀卷积网络,结合可变形卷积动态捕捉细节纹理边缘和膨胀卷积增强纹理远距离关联性,有效恢复深层特征捕捉壁画笔触细节。设计双域网络,通过全局注意力分支建模壁画整体色调协调性,局部分组卷积分支强化笔触细节,解决迁移过程中的信息丢失和色彩层次弱化问题。提出路径融合网络,利用多膨胀率深度可分离卷积并行处理与动态门控融合机制,优化元素间逻辑关系与比例协调。研究表明,本文所提方法在FID、LPIPS和L2损失指标上分别降低了5.81%、4.36%和5.73%,而SSIM提升了8.12%。用户调研显示,其在内容保真度、风格匹配度和视觉吸引力方面表现突出。本文方法有效解决了迁移敦煌壁画时色彩层次、纹理细节与空间布局的保留难题,可为敦煌壁画艺术的数字化保护与创新传播提供新的思路。

Abstract:

To address color distortion, detail blurring, and structural incoherence in existing style transfer techniques when processing Dunhuang murals——caused by highly saturated mineral pigments, intricate textures, and complex layered structures——this paper proposes a Multi-scale Dunhuang Style Transfer Network based on an improved Cycle-Consistent Generative Adversarial Network for high-quality artistic style transfer. We introduce an adaptive local dilated convolutional-net that dynamically captures detailed texture edges using deformable convolution and enhances long-range texture dependencies through dilated convolution, thereby restoring deep features to preserve brushstroke details. A dual scope net is designed to mitigate information loss and color-layer weakening during style transfer, employing a global attention branch to model overall tonal harmony and a local grouped convolution branch to reinforce stroke details. Additionally, a pathwise fusion net optimizes logical relationships and proportional coordination between elements using multi-dilation-rate depthwise separable convolutions for parallel processing and a dynamic gated fusion mechanism. Experimental results show that the proposed method achieves reductions of 5.81%, 4.36%, and 5.73% in FID, LPIPS, and L2 loss, respectively, and an improvement of 8.12% in SSIM. User studies confirm its superiority in content fidelity, style consistency, and visual appeal. This approach effectively resolves challenges in preserving color layers, texture details, and spatial layouts in Dunhuang murals transfer, offering a novel approach for Dunhuang murals digitization and innovative dissemination.