论文标题
通过结合全球和本地损失，任意风格转移与结构增强
Arbitrary Style Transfer with Structure Enhancement by Combining the Global and Local Loss
论文作者
论文摘要
任意风格的转移生成了艺术图像，该图像仅使用一个训练有素的网络结合了内容图像的结构和艺术品的艺术风格。此方法中使用的图像表示包含内容结构表示和样式模式表示形式，这通常是预训练的分类网络中高级表示的特征表示。但是，传统的分类网络的设计用于分类，通常集中于高级功能并忽略其他功能。结果，风格化的图像在整个图像中均匀地分布了样式元素，并使整体图像结构无法识别。为了解决这个问题，我们通过结合全球和局部损失引入了一种新型的任意风格转移方法，并通过结构增强。局部结构细节由LapStyle表示，全局结构由图像深度控制。实验结果表明，与其他最新方法相比，我们的方法可以在几个常见数据集中生成具有令人印象深刻的视觉效果的更高质量图像。
Arbitrary style transfer generates an artistic image which combines the structure of a content image and the artistic style of the artwork by using only one trained network. The image representation used in this method contains content structure representation and the style patterns representation, which is usually the features representation of high-level in the pre-trained classification networks. However, the traditional classification networks were designed for classification which usually focus on high-level features and ignore other features. As the result, the stylized images distribute style elements evenly throughout the image and make the overall image structure unrecognizable. To solve this problem, we introduce a novel arbitrary style transfer method with structure enhancement by combining the global and local loss. The local structure details are represented by Lapstyle and the global structure is controlled by the image depth. Experimental results demonstrate that our method can generate higher-quality images with impressive visual effects on several common datasets, comparing with other state-of-the-art methods.
