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<p>Thangka murals, as a vital carrier of Tibetan Buddhist culture, pose unique challenges for automatic image colorization due to their intricate structures and rich symbolic content. To address the critical issues of color overflow and semantic inconsistency in Thangka image colorization, we propose MACColor, a novel end-to-end framework that integrates two key components: (1) a Multi-Scale Adaptive Color-Constrained Attention (MACCA) module to enforce color locality and suppress overflow; (2) a Cross-Dimensional Synergistic Attention (CDSA) module to enhance semantic coherence by jointly modeling spatial, channel, and scale interactions. In addition, we build a dedicated Thangka grayscale colorization dataset, consisting of 8500 high-resolution images (512 × 512), to support future research in this domain. Extensive experiments demonstrate that our method achieves superior performance compared to existing approaches on both objective metrics and subjective evaluations, producing visually vivid and semantically consistent colorization results, while effectively constraining color diffusion and introducing a lightweight MACColor-Tiny variant for practical applications.</p>

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MACColor: multi-scale and cross-dimensional attention for thangka image colorization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月18日

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  • Zhen Wang,
  • Nianyi Wang,
  • Yunbo Yang,
  • Xinyang Zhang,
  • Mengyuan Zhang,
  • Yutong Wang

摘要

Thangka murals, as a vital carrier of Tibetan Buddhist culture, pose unique challenges for automatic image colorization due to their intricate structures and rich symbolic content. To address the critical issues of color overflow and semantic inconsistency in Thangka image colorization, we propose MACColor, a novel end-to-end framework that integrates two key components: (1) a Multi-Scale Adaptive Color-Constrained Attention (MACCA) module to enforce color locality and suppress overflow; (2) a Cross-Dimensional Synergistic Attention (CDSA) module to enhance semantic coherence by jointly modeling spatial, channel, and scale interactions. In addition, we build a dedicated Thangka grayscale colorization dataset, consisting of 8500 high-resolution images (512 × 512), to support future research in this domain. Extensive experiments demonstrate that our method achieves superior performance compared to existing approaches on both objective metrics and subjective evaluations, producing visually vivid and semantically consistent colorization results, while effectively constraining color diffusion and introducing a lightweight MACColor-Tiny variant for practical applications.