一种基于GAN的轻量级水墨画风格迁移模型

当前现有的风格迁移方法大多以照片或西方绘画为主。由于中西方画作之间的内在差异,直接应用现有的算法无法生成令人满意的中国水墨画风格迁移的结果。文中基于GAN提出了一种新颖的适用于水墨画的风格迁移方法。该方法结合了AdaIN方法、风格注意力模块和感知损失,可以更准确地学习到水墨画的风格特征,一定程度上解决了水墨图像生成质量不佳的问题。定性分析和定量评估结果表明文中方法性能更好,生成的结果具有更佳的视觉效果。相比于基准方法,文中所提方法减少了约55%的参数量,降低了约60%的训练时间。

A GAN-Based Lightweight Style Transfer Model for Ink Painting

Current style transfer methods are mainly suitable for photos or Western paintings. Due to the inherent differences between Chinese and Western paintings, existing algorithms cannot generate satisfactory results when applied to the style transfer task of Chinese ink paintings. Consequently, a novel style transfer method based on GAN for ink painting is proposed in the study. This method combines AdaIN, style-attention module and perceptual loss to learn the style features of ink painting more accurately, thereby solving the problem that the generated results of ink painting have poor quality. According to the qualitative analysis and quantitative evaluation, experimental results show that this method has better performance and the generated results have higher visual quality. Compared with the baseline, the method reduces the number of parameters by about 55% and the training time by about 60%.