内容语义和风格特征匹配一致的艺术风格迁移

Content semantics and style features match consistent artistic style transfer

随着计算机视觉领域的发展,图像风格迁移已经成为一个具有挑战性和研究价值的重要课题。针对现有方法无法有效保留内容图像物体轮廓和同种内容语义迁移多种不同风格特征的问题,提出了一个内容语义和风格特征匹配一致的艺术风格迁移网络。首先,利用双支路特征处理模块增强风格特征和内容特征,并保留内容图像的物体轮廓;然后,在注意力特征空间中实现特征分布对齐和融合;最后,采用具有空间感知能力的插值模块实现内容语义的风格一致化。使用82783张真实照片和80095张艺术画像进行风格迁移训练,另各使用1000张真实照片和艺术画像进行测试。实验通过与最新的4种风格迁移方法进行比较,并进行消融实验分别验证该框架与所加损失函数的有效性。实验结果表明,本文网络在256像素图像生成中平均运行时间为9.42 ms,在512像素图像生成中平均运行时间为10.23 ms;同时避免了内容结构扭曲失真,并将内容语义和风格特征匹配一致,具有更好的艺术视觉效果。

The development of computer vision has rendered image style transfer a challenging and valuable subject of research.Nonetheless,existing methods are unable to effectively preserve object contours of content images while migrating many different style features with the same content semantics.In response,an artistic style transfer network,with consistent matching of content semantics and style features,was proposed.First,a two-branch feature processing module was employed to enhance the style and content features and retain the object contours of content images.Subsequently,feature distribution alignment and fusion were achieved within the attentional feature space.Finally,an interpolation module with spatial perception capability was utilized to achieve style consistency of content semantics.The network was trained with 82783 actual photos and 80095 artistic portraits for style transfer.Furthermore,1000 actual photos and 1000 artistic portraits were used for testing.The effectiveness of the proposed framework and the added loss function was verified through experiments,which included comparing it with the latest four style transfer methods and conducting ablation experiments,respectively.The experimental results demonstrated that the proposed network could run at an average time of 9.42 ms in 256-pixel image generation and 10.23 ms in 512-pixel image generation,while avoiding distortion of content structure and matching content semantics and style features consistently,with better artistic visual effects.