通过艺术设计保护和增强文化遗产,使用特征金字塔网络,通过修改的建造者优化算法进行优化
1School of Arts, University of Technology and Education, Tianjin, 300222, China. zsf1234789@edu.yadx.
Scientific reports
|November 28, 2025
概括
使用特征金字塔网络 (FPN) 和修改的构建者优化算法 (MBOA) 的新深度学习框架有效地分类,恢复和合成文化艺术品,增强数字保存方法.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 数字人文学科 数字人文学科
背景情况:
- 文化遗产越来越受到环境,社会和政治因素的威胁.
- 现有的数字保存方法需要加强,以应对这些不断变化的威胁.
研究的目的:
- 引入一种新的深度学习框架,用于对具有文化意义的艺术品进行分类,修复和合成.
- 提高数字艺术保存和创作的准确性和效率.
主要方法:
- 利用特征金字塔网络 (FPN) 来进行多尺度的特征提取,捕获精细的纹理和全球语义.
- 通过生物启发的搜索策略,使用修改的构建者优化算法 (MBOA) 进行超参数优化.
- 利用神经风格转移来产生受文化美学启发的新艺术品.
主要成果:
- 在风格分类中实现了96.7%的准确性.
- 在修复任务中获得的峰值信号噪声比 (PSNR) 为32.41dB,结构相似度指数 (SSIM) 为0.937.
- 创建的艺术品具有Fréchet初始距离 (FID) 评分14.3,超过了最先进的模型.
结论:
- 拟议的框架在恢复退化的艺术品和产生新的文化灵感作品方面表现出卓越的表现.
- 这种方法提高了档案的真实性和质量,为文化机构提供了可扩展的工具,并促进了与遗产资产的创造性互动.
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