SRAGAN：用于中国水墨画风格迁移的显著性正则化与注意力生成对抗网络
SRAGAN: Saliency Regularized and Attended Generative Adversarial Network for Chinese Ink-wash Painting Style Transfer
AI总结：
本文提出SRAGAN解决中国水墨画风格迁移中内容细节易被破坏的问题，通过引入显著性检测提出SIOU损失和SANorm正则化内容结构，并设计显著性注意力判别器增强笔触与纹理，实验证明其优于GAN和扩散模型等先进方法。
摘要：
近期风格迁移问题在跨域图像到图像（I2I）翻译视角下仍主要由生成对抗网络（GAN）主导，其核心问题是学习目标域风格模式并将其迁移至源域内容图像上。本文处理将真实照片转换为传统中国水墨画的问题，即中国水墨画风格迁移。尽管大量I2I模型处理了此问题，但一个显著挑战是源图像的内容细节会因水墨画风格元素的迁移而轻易被抹除或破坏。为解决此问题，我们提出将显著性检测引入非配对I2I框架以正则化图像内容，其中检测到的显著性图从两方面被利用：(1)我们提出显著性IOU（SIOU）损失，通过在图像风格化前后强制显著性一致性，显式正则化对象内容结构；(2)我们提出显著性自适应归一化（SANorm），通过将图像显著性信息动态注入生成器以指导风格化过程，隐式增强生成画作的对象结构完整性。此外，我们还提出显著性注意力判别器，利用图像显著性信息将生成对抗注意力集中于所绘对象，有助于生成更生动细腻的笔触与水墨纹理。大量定性和定量实验证明了我们的方法在GAN和扩散模型范式上均优于相关先进图像风格化方法。
英文摘要：
Recent style transfer problems are still largely dominated by Generative Adversarial Network (GAN) from the perspective of cross-domain image-to-image (I2I) translation, where the pivotal issue is to learn and transfer target-domain style patterns onto source-domain content images. This paper handles the problem of translating real pictures into traditional Chinese ink-wash paintings, i.e., Chinese ink-wash painting style transfer. Though a wide range of I2I models tackle this problem, a notable challenge is that the content details of the source image could be easily erased or corrupted due to the transfer of ink-wash style elements. To remedy this issue, we propose to incorporate saliency detection into the unpaired I2I framework to regularize image content, where the detected saliency map is utilized from two aspects: (\romannumeral1) we propose saliency IOU (SIOU) loss to explicitly regularize object content structure by enforcing saliency consistency before and after image stylization; (\romannumeral2) we propose saliency adaptive normalization (SANorm) which implicitly enhances object structure integrity of the generated paintings by dynamically injecting image saliency information into the generator to guide stylization process. Besides, we also propose saliency attended discriminator which harnesses image saliency information to focus generative adversarial attention onto the drawn objects, contributing to generating more vivid and delicate brush strokes and ink-wash textures. Extensive qualitative and quantitative experiments demonstrate superiority of our approach over related advanced image stylization methods in both GAN and diffusion model paradigms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
