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<p>Line drawing, characterized by its unique techniques and profound cultural heritage, presents the boundless charm of traditional Chinese art. To gain deeper insights into the esthetic styles of different dynasties and the essence of various painting schools, employing digital methods to extract the contours of relics is essential. Although existing digital line drawing extraction methods can enhance efficiency, the surfaces of relics often exhibit pigment mottling, cracks, and other diseases. These interfering elements tend to intertwine with the contours, increasing the difficulty of line extraction. For this task, we propose a knowledge-sharing strategy-based approach to learn the mapping from relic images to contours in a semi-supervised manner. We integrate a supervised learning branch into the unsupervised image translation framework that utilizes undamaged relic images paired with corresponding noise-free contours for training. By sharing the generator, the noise-free contour style learned by the supervised branch is infused into the unsupervised branch, effectively suppressing diseases. Furthermore, we achieve complementary texture features through two independent streams: the contour generation flow (CGF) and the auxiliary gradient flow (AGF), each designed with separate GAN architectures. To better capture relic details, we introduce the coordinate attention transfer (CAT) module, which adaptively transfers features from AGF to the corresponding dimensions of CGF by learning spatial orientation and positional weights. Finally, the bidirectional gated transposed fusion (Bi-GTF) module employs two soft gates to regulate the information fusion between CGF and AGF, guiding CGF to focus on the shape and intricate details of the relics. In our experiments, we conducted both quantitative and qualitative comparisons with six state-of-the-art algorithms, analyzing the impact of gradient-guided feature transfer and the generalization of the semi-supervised framework. The results demonstrate that Relic2Contour generates rich, clear, and high-quality contour images from relics across various styles.</p>

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Relic2Contour: Salient contour detection in relics using semi-supervised generative adversarial networks based on knowledge sharing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月24日

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  • Zhe Yu,
  • Qingqing Kang,
  • Jun Wang,
  • Shenglin Peng,
  • Qunxi Zhang,
  • Yirong Ma,
  • Shuyi Qu,
  • Jinye Peng,
  • Xianlin Peng

摘要

Line drawing, characterized by its unique techniques and profound cultural heritage, presents the boundless charm of traditional Chinese art. To gain deeper insights into the esthetic styles of different dynasties and the essence of various painting schools, employing digital methods to extract the contours of relics is essential. Although existing digital line drawing extraction methods can enhance efficiency, the surfaces of relics often exhibit pigment mottling, cracks, and other diseases. These interfering elements tend to intertwine with the contours, increasing the difficulty of line extraction. For this task, we propose a knowledge-sharing strategy-based approach to learn the mapping from relic images to contours in a semi-supervised manner. We integrate a supervised learning branch into the unsupervised image translation framework that utilizes undamaged relic images paired with corresponding noise-free contours for training. By sharing the generator, the noise-free contour style learned by the supervised branch is infused into the unsupervised branch, effectively suppressing diseases. Furthermore, we achieve complementary texture features through two independent streams: the contour generation flow (CGF) and the auxiliary gradient flow (AGF), each designed with separate GAN architectures. To better capture relic details, we introduce the coordinate attention transfer (CAT) module, which adaptively transfers features from AGF to the corresponding dimensions of CGF by learning spatial orientation and positional weights. Finally, the bidirectional gated transposed fusion (Bi-GTF) module employs two soft gates to regulate the information fusion between CGF and AGF, guiding CGF to focus on the shape and intricate details of the relics. In our experiments, we conducted both quantitative and qualitative comparisons with six state-of-the-art algorithms, analyzing the impact of gradient-guided feature transfer and the generalization of the semi-supervised framework. The results demonstrate that Relic2Contour generates rich, clear, and high-quality contour images from relics across various styles.