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卡超分辨率扩散模型基于离散的等号变换域填充上采样和高频聚焦注意力模型
Xin Chen1, Liqi Ji1, Zhen Wang1
1Key Laboratory of Linguistic and Cultural Computing, Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China.
PloS one
|September 25, 2025
概括
一种新的频域增强扩散超分辨率 (FDEDiff) 方法通过专注于高频细节和使用DCT上采样来改善Thangka图像恢复. 这种数字保存技术增强了文化遗产应用的视觉感知.
科学领域:
- 计算机视觉 计算机视觉
- 数字图像处理 数字图像处理
- 保护文化遗产 保护文化遗产
背景情况:
- 卡藏族传统绘画具有重要的文化价值和独特的艺术风格.
- 现有的图像超分辨率方法与Thangka图像的大尺寸和复杂的细节作斗争,导致质地恢复和感知质量差.
- 数字化保存和恢复对于维护像卡艺术这样的文化遗产的完整性至关重要.
研究的目的:
- 为了解决当前超分辨率技术对Thangka图像的局限性.
- 为了提高重建的高频细节和复杂的纹理在损坏的Thangka图像.
- 为了提高超分辨率的Thangka图像的感知质量,以保护文化.
主要方法:
- 提出了一种频域增强扩散超分辨率 (FDEDiff) 方法.
- 引入了一个高频聚焦交叉注意力机制 (HFC-Attention) 来指导具有高频特征的扩散模型.
- 实施DCT域填充采样 (DCT-Upsampling) 以使用全球信息改进重建密度线区域.
- 构建了一个新的Thangka图像超分辨率数据集,包含82,688个图像对.
主要成果:
- FDEDiff在Thangka数据集上取得了最先进的表现.
- 该方法获得了0.0815的LPIPS得分,这表明了卓越的感知质量.
- 与基线方法相比,感知质量有20%的改善.
- 成功重建高频细节和复杂的纹理,增强视觉真实性.
结论:
- FDEDiff有效地克服了超高分辨率Thangka图像的挑战,特别是在细节和纹理重建方面.
- 拟议的方法显著提高了感知质量,使其适合于文化遗产的保护.
- 虽然FDEDiff的推断时间较长,但其增强的艺术真实性证明了其在关键的文化应用中使用的合理性.
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