Image style transfer renders the content of an image into different styles. Current methods made decent progress with transferring the style of single image, however, visual statistics from one image cannot reflect the full scope of an artist. Also, previous work did not put content preservation in the important position, which would result in poor structure integrity, thus deteriorating the comprehensibility of generated image. These two problems would limit the visual quality improvement of style transfer results. Targeting at style resemblance and content preservation problems, we propose a style transfer system composed of collection representation space and semantic-guided reconstruction. We train an encoder–decoder network with art collections to construct a representation space that can reflect the style of the artist. Then, we use semantic information as guidance to reconstruct the target representation of the input image for better content preservation. We conduct both quantitative analysis and qualitative evaluation to assess the proposed method. Experiment results demonstrate that our approach well balanced the trade-off between capturing artistic characteristics and preserving content information in style transfer tasks.

图像样式传输将图像的内容呈现为不同的样式。当前的方法在转移单个图像的样式方面取得了可观的进步，但是，一张图像的视觉统计不能反映艺术家的全部范围。而且，先前的工作没有将内容保存放在重要位置，这将导致较差的结构完整性，从而使生成的图像的可理解性变差。这两个问题将限制样式转移结果的视觉质量改善。针对样式相似和内容保存问题，我们提出了一种由集合表示空间和语义引导重构组成的样式传递系统。我们训练带有艺术品收藏的编码器/解码器网络，以构建可以反映艺术家风格的代表空间。然后，我们使用语义信息作为指导来重构输入图像的目标表示，以更好地保存内容。我们进行定量分析和定性评估，以评估所提出的方法。实验结果表明，我们的方法很好地平衡了在捕获艺术特征和保留样式传递任务中的内容信息之间的权衡。