Transferring a photo to a stylised image with beautiful texture has become one of the most popular topics in computer vision and the application of image processing. Controlling the stroke size of the texture is one of the challenging problems in this task. Recent representative methods for such problem introduce a pyramid model to regulate receptive fields in the network. Meanwhile, dilated convolutions are proved to be a very efficient way to adjust receptive fields without losing resolution. By combining the advantages of both approaches and making special optimisation for VGG19 model for style transfer tasks, the authors propose to exploit dilated convolutions to extract texture information endowing the network with stroke controllable. Several sets of contrast experiments were conducted and results show that their algorithm can generate more attractive stylisation images and control stroke size flexibly. It demonstrates the superiority of applying dilated convolutions as a texture extraction method for maintaining more texture information and controlling stroke size.

中文翻译：

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基于膨胀卷积的笔画可控样式转换

将照片转移到具有漂亮纹理的风格化图像上已成为计算机视觉和图像处理应用程序中最受欢迎的主题之一。控制纹理的笔触大小是此任务中的难题之一。针对该问题的最新代表性方法引入了金字塔模型来调节网络中的接收场。同时，膨胀卷积被证明是一种在不损失分辨率的情况下调整接收场的非常有效的方法。通过结合这两种方法的优点，并对样式转换任务的VGG19模型进行特殊优化，作者建议利用膨胀卷积来提取可通过笔划控制的网络纹理信息。进行了几组对比实验，结果表明它们的算法可以生成更具吸引力的样式化图像并灵活地控制笔触大小。它证明了应用扩张卷积作为纹理提取方法的优势，该方法可以保持更多纹理信息并控制笔触大小。