IEEE Trans Image Process. 2017 May;26(5):2338-2351. doi: 10.1109/TIP.2017.2678168. Epub 2017 Mar 8.
Style transfer is a process of migrating a style from a given image to the content of another, synthesizing a new image, which is an artistic mixture of the two. Recent work on this problem adopting convolutional neural-networks (CNN) ignited a renewed interest in this field, due to the very impressive results obtained. There exists an alternative path toward handling the style transfer task, via the generalization of texture synthesis algorithms. This approach has been proposed over the years, but its results are typically less impressive compared with the CNN ones. In this paper, we propose a novel style transfer algorithm that extends the texture synthesis work of Kwatra et al. (2005), while aiming to get stylized images that are closer in quality to the CNN ones. We modify Kwatra's algorithm in several key ways in order to achieve the desired transfer, with emphasis on a consistent way for keeping the content intact in selected regions, while producing hallucinated and rich style in others. The results obtained are visually pleasing and diverse, shown to be competitive with the recent CNN style transfer algorithms. The proposed algorithm is fast and flexible, being able to process any pair of content + style images.
风格迁移是一种将给定图像的风格迁移到另一个图像内容的过程，合成一个新的图像，这是两种艺术的混合。最近采用卷积神经网络（CNN）的这个问题的工作激发了人们对该领域的重新兴趣，因为获得了非常令人印象深刻的结果。通过纹理合成算法的泛化，存在一种处理风格迁移任务的替代途径。多年来提出了这种方法，但其结果通常不如 CNN 的结果令人印象深刻。在本文中，我们提出了一种新颖的风格迁移算法，该算法扩展了 Kwatra 等人（2005 年）的纹理合成工作，旨在获得质量更接近 CNN 的风格化图像。我们以保持选定区域内容完整的一致方式修改了 Kwatra 的算法，同时在其他区域产生幻觉和丰富的风格。所获得的结果令人赏心悦目，多种多样，与最近的 CNN 风格迁移算法具有竞争力。所提出的算法快速灵活，能够处理任何内容+风格的图像对。
