多维感知引导的代反射去除网络,具有深度功能,用于绘制图像.
Yuqi Xie1,2, Xiaojuan Zhang3,4, Yang Zhao5
1School of Computer Science, Qinghai Normal University, Xining, 810016, China.
Scientific reports
|September 29, 2025
概括
这项研究介绍了MPGINet,这是一个代网络,用于从数字化Thangka绘画中去除玻璃盖反射. 这种新的方法显著提高了图像质量,保留了艺术品的细节.
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
- 艺术 保护 技术 技术
背景情况:
- 由于玻璃盖反射导致的图像质量下降,Thangka绘画的数字化受到阻碍.
- 现有的方法很难有效地去除这些反射而不损害艺术品细节.
研究的目的:
- 开发一个创新的代预测网络 (MPGINet) 以在卡绘画数字化中进行强大的反射移除.
- 通过解决反射诱导的艺术品来增强数字化艺术品的视觉真实性.
主要方法:
- 提出了一个代预测网络 (MPGINet),利用多维深度特征和反射感知.
- 采用了两级架构:U-Net与Squeeze-and-Excitation用于反射层精制,以及深度特征金字塔网络 (DFPN)用于传输层恢复.
- 集成的频域信息分离和面具引导的图像 inpainting 增强的反射去除.
主要成果:
- 在Thangka数据集上,MPGINet的PSNR为28.90dB,SSIM为0.962,比最先进的 (SOTA) 方法的性能优于1.88dB和0.027.
- 证明了对自然场景数据集的概括能力,与SOTA方法取得了可比的结果.
结论:
- MPGINet有效地消除了玻璃盖的反射,大大提高了数字化Thangka绘画的图像质量.
- 该网络的架构结合了深度功能融合和代改进,确保了详细的修复,并保留了艺术品的真实性.
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