  Dunhuang murals are a collection of Chinese style and national style, forming
a self-contained Chinese-style Buddhist art. It has very high historical and
cultural value and research significance. Among them, the lines of Dunhuang
murals are highly general and expressive. It reflects the character's
distinctive character and complex inner emotions. Therefore, the outline
drawing of murals is of great significance to the research of Dunhuang Culture.
The contour generation of Dunhuang murals belongs to image edge detection,
which is an important branch of computer vision, aims to extract salient
contour information in images. Although convolution-based deep learning
networks have achieved good results in image edge extraction by exploring the
contextual and semantic features of images. However, with the enlargement of
the receptive field, some local detail information is lost. This makes it
impossible for them to generate reasonable outline drawings of murals. In this
paper, we propose a novel edge detector based on self-attention combined with
convolution to generate line drawings of Dunhuang murals. Compared with
existing edge detection methods, firstly, a new residual self-attention and
convolution mixed module (Ramix) is proposed to fuse local and global features
in feature maps. Secondly, a novel densely connected backbone extraction
network is designed to efficiently propagate rich edge feature information from
shallow layers into deep layers. Compared with existing methods, it is shown on
different public datasets that our method is able to generate sharper and
richer edge maps. In addition, testing on the Dunhuang mural dataset shows that
our method can achieve very competitive performance.
