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.
翻译:Dunhuang 壁画是中国风格和民族风格的集合,形成中国风格的佛教艺术,具有很高的历史和文化价值和研究意义,其中,Dunhuang 壁画的线条非常笼统和直观,反映了人性特点和复杂的内情。因此,壁画的轮廓图画对Dunhuang 文化的研究具有重大意义。Dunhuang 壁画的轮廓生成属于图像边缘探测,这是计算机视觉的一个重要分支,目的是在图像中提取突出的等距信息。尽管基于革命的深层次学习网络通过探索图像的背景和语义特征,在图像边缘提取方面取得了良好的效果。然而,随着容取域的扩展,一些本地细节信息丢失了。在这个文件中,我们提出了一种新型的边际探测仪,它与更深层次探测方法相比,首先,以深层次深层次深层次的深层次学习网络的边际图像提取取得了良好的效果。 一种新的内深层次的内深层次的内深层次数据采集方法,是从我们所设计的内深层次的内深层次的内深层次的内深层次的内深层次数据测试,这是我们所设计的内深层次的内深层次的内深层次的内深层次的内深层次的内深层次的内建的内模和深层次的内深层次数据测试。