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<p>Ancient murals often suffer from degradation, compromising their cultural significance. To address this, we propose AFMNet, a lightweight network based on a multi-head attention mechanism for high-quality mural completion. AFMNet effectively integrates multi-scale features and captures long-range dependencies, enhancing semantic consistency. Extensive experiments on a custom mural dataset and the Places2 benchmark demonstrate its superiority over competitive methods. Quantitatively, our method achieves a Peak Signal-to-Noise Ratio (PSNR) of 34.42 dB on murals with low degradation (1-10% missing area) and reduces the parameter count by 34.4% compared to the baseline, showcasing its high efficiency. The results exhibit excellent visual fidelity with fewer artifacts, providing a valuable tool for the digital preservation of ancient murals.</p>

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AFMNet: an activation-free mural completion network via multi-head attention mechanism

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月13日

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  • Chunmei Hu,
  • Ding Luo,
  • Guofang Xia,
  • Ziyue You,
  • Xinjian Ma,
  • Xi Liu

摘要

Ancient murals often suffer from degradation, compromising their cultural significance. To address this, we propose AFMNet, a lightweight network based on a multi-head attention mechanism for high-quality mural completion. AFMNet effectively integrates multi-scale features and captures long-range dependencies, enhancing semantic consistency. Extensive experiments on a custom mural dataset and the Places2 benchmark demonstrate its superiority over competitive methods. Quantitatively, our method achieves a Peak Signal-to-Noise Ratio (PSNR) of 34.42 dB on murals with low degradation (1-10% missing area) and reduces the parameter count by 34.4% compared to the baseline, showcasing its high efficiency. The results exhibit excellent visual fidelity with fewer artifacts, providing a valuable tool for the digital preservation of ancient murals.