Abstract
Style transfer is adopted to synthesize appealing stylized images that preserve the structure of a content image but carry the pattern of a style image. Many recently proposed style transfer methods use only western oil paintings as style images to achieve image stylization. As a result, unnatural messy artistic effects are produced in stylized images when using these methods to directly transfer the patterns of traditional Chinese paintings, which are composed of plain colors and abstract objects. Moreover, most of them work only at the original image scale and thus ignore multiscale image information during training. In this paper, we present a novel effective multiscale style transfer method based on Laplacian pyramid decomposition and reconstruction, which can transfer unique patterns of Chinese paintings by learning different image features at different scales. In the first stage, the holistic patterns are transferred at low resolution by adopting a Style Transfer Base Network. Then, the details of the content and style are gradually enhanced at higher resolutions by a Detail Enhancement Network with an edge information selection (EIS) module in the second stage. The effectiveness of our method is demonstrated through the generation of appealing high-quality stylization results and a comparison with some state-of-the-art style transfer methods. Datasets and codes are available at this https URL.
Abstract (translated)
风格转换技术被用来合成具有吸引力的、保留内容图像结构但携带样式图像图案的艺术化图片。近年来，许多提出的风格转换方法仅使用西方油画作为样式图像来实现图像艺术化处理。因此，在直接应用这些方法转移中国传统绘画（由单纯颜色和抽象物体组成）中的模式时，会产生不自然且杂乱的艺术效果。此外，大多数现有方法只在原始图像尺度上工作，因而忽略了训练过程中的多尺度图像信息。 本文提出了一种基于拉普拉斯金字塔分解与重构的全新有效的多尺度风格转换方法，能够通过学习不同尺度下的不同图像特征来转移中国绘画的独特模式。首先，在第一阶段使用风格传输基础网络以低分辨率进行整体图案的传递；然后在第二阶段中，细节增强网络利用边缘信息选择（EIS）模块在更高分辨率下逐步强化内容和样式的细节。 我们方法的有效性通过生成高质量且具有吸引力的艺术化结果以及与一些最先进的风格转换方法对比来证明。数据集和代码可在以下网址获取：[此处提供实际链接]。
URL
https://arxiv.org/abs/2502.04597
