  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.
