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<p>The preservation of cultural artifacts is vital for maintaining historical continuity, particularly for traditional Chinese paintings that often suffer from decay and damage over time. Existing inpainting methods struggle to simultaneously recover complex brushwork structures, maintain visual coherence, and preserve consistency across multiple resolutions. To address these challenges, we present the Twin Cascade Spatial Multi-scale Attention Filtering (TCSMAF) method, which adopts a symmetric multi-scale dual-branch architecture to capture complex structures and semantic details through parallel processing. A Spatial Kernel Module is proposed to enhance spatial perception by coordinating hierarchical features with spatial coordinate encoding. Moreover, a Multi-scale Spatial and Channel Attention module that adopts progressive convolution kernel sizes is introduced to improve texture reconstruction by leveraging features across different scales and channels. These technical innovations significantly advance digital inpainting methodologies, providing a robust framework specifically designed to handle the intricate textures and details of damaged paintings. The dataset and code are available at <a href="https://github.com/LPDLG/TCSMAF">https://github.com/LPDLG/TCSMAF</a>.</p>

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TCSMAF: twin cascade spatial multi-scale attention filtering inpainting of traditional Chinese painting

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月8日

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  • Qiyao Hu,
  • Lingjie Kong,
  • Chao Li,
  • Xianlin Peng,
  • Shuyi Qu,
  • Jinye Peng

摘要

The preservation of cultural artifacts is vital for maintaining historical continuity, particularly for traditional Chinese paintings that often suffer from decay and damage over time. Existing inpainting methods struggle to simultaneously recover complex brushwork structures, maintain visual coherence, and preserve consistency across multiple resolutions. To address these challenges, we present the Twin Cascade Spatial Multi-scale Attention Filtering (TCSMAF) method, which adopts a symmetric multi-scale dual-branch architecture to capture complex structures and semantic details through parallel processing. A Spatial Kernel Module is proposed to enhance spatial perception by coordinating hierarchical features with spatial coordinate encoding. Moreover, a Multi-scale Spatial and Channel Attention module that adopts progressive convolution kernel sizes is introduced to improve texture reconstruction by leveraging features across different scales and channels. These technical innovations significantly advance digital inpainting methodologies, providing a robust framework specifically designed to handle the intricate textures and details of damaged paintings. The dataset and code are available at https://github.com/LPDLG/TCSMAF.