As the first step of the restoration process of painted relics, sketch extraction plays an important role in cultural research. However, sketch extraction suffers from serious disease corrosion, which results in broken lines and noise. To overcome these problems, we propose a deep learning-based hierarchical sketch extraction framework for painted cultural relics. We design the sketch extraction process into two stages: coarse extraction and fine extraction. In the coarse extraction stage, we develop a novel detail-aware bi-directional cascade network that integrates flow-based difference-of-Gaussians (FDoG) edge detection and a bi-directional cascade network (BDCN) under a transfer learning framework. It not only uses the pre-trained strategy to extenuate the requirements of large datasets for deep network training but also guides the network to learn the detail characteristics by the prior knowledge from FDoG. For the fine extraction stage, we design a new multiscale U-Net (MSU-Net) to effectively remove disease noise and refine the sketch. Specifically, all the features extracted from multiple intermediate layers in the decoder of MSU-Net are fused for sketch predication. Experimental results showed that the proposed method outperforms the other seven state-of-the-art methods in terms of visual and quantitative metrics and can also deal with complex backgrounds.

作为彩绘文物修复过程的第一步，素描提取在文化研究中起着重要作用。但是，草图提取会遭受严重的疾病腐蚀，从而导致折线和噪音。为了克服这些问题，我们提出了一种基于深度学习的，用于彩绘文物的分层草图提取框架。我们将草图提取过程设计为两个阶段：粗略提取和精细提取。在粗略提取阶段，我们开发了一种新颖的基于细节的双向双向级联网络，该网络在转移学习框架下将基于流的高斯差分（FDoG）边缘检测与双向级联网络（BDCN）集成在一起。它不仅使用预训练的策略来减轻大型数据集对深度网络训练的要求，而且可以通过FDoG的先验知识引导网络学习细节特征。在精细提取阶段，我们设计了一个新的多尺度U-Net（MSU-Net），以有效消除疾病噪声并优化草图。具体来说，将从MSU-Net解码器中多个中间层提取的所有特征融合在一起，以进行草图预测。实验结果表明，在视觉和定量指标方面，该方法优于其他七个最新方法，并且还可以处理复杂的背景。我们设计了一种新的多尺度U-Net（MSU-Net），以有效消除疾病噪声并优化草图。具体来说，将从MSU-Net解码器中多个中间层提取的所有特征融合在一起，以进行草图预测。实验结果表明，在视觉和定量指标方面，该方法优于其他七个最新方法，并且还可以处理复杂的背景。我们设计了一种新的多尺度U-Net（MSU-Net），以有效消除疾病噪声并优化草图。具体来说，将从MSU-Net解码器中多个中间层提取的所有特征融合在一起，以进行草图预测。实验结果表明，在视觉和定量指标方面，该方法优于其他七个最新方法，并且还可以处理复杂的背景。