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Dunhuang Mural Line Drawing Based on Multi-scale Feature Fusion and Sharp Edge Learning

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Abstract

Dunhuang murals are the treasures of world culture and art, world-famous and extremely valuable for research. However, this treasure of human art has become extremely fragile due to environmental factors, major natural disasters and human destruction. As a result, the conservation and restoration of Dunhuang murals has become a task of great urgency, in which line drawings of Dunhuang murals occupy most of the time, and subsequent restoration work is guided by line drawings. The line drawings of the Dunhuang murals can be regarded as an image edge detection task, which is one of the most fundamental problem for advanced computer vision tasks. Its aim is to accurately extract object contours and meaningful in-object edges from images. Although many CNN-based edge detection methods have made significant progress on natural images, they still produce thick edges and non-edges when generating line drawings of Dunhuang murals. In this paper, we propose a novel edge detection method for generating line drawings of Dunhuang murals to address these problems. We first propose a novel loss function for edge detection, which effectively suppresses background noise pixels located near edges and enables the network to produce sharp edges. In addition, to take full advantage of the hierarchical features at different scales, we introduce DCM and SAM on the basis of the bottom-up/top-down structure. Finally, the results of experiments conducted on the BIPED dataset and the Dunhuang murals dataset show that the proposed method can generate richer and sharper edge maps.

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Acknowledgements

This work was supported in part by the Gansu Provincial Department of Education University Teachers Innovation Fund Project (No.2023B-056), the Fundamental Research Funds for the Central Universities (No. 31920230137, 31920220130, 31920230030, 31920220037), the Introduction of Talent Research Project of Northwest Minzu University (No. xbmuyjrc201904), the Gansu Provincial First-class Discipline Program of Northwest Minzu University (No.11080305), the Leading Talent of National Ethnic Affairs Commission (NEAC), and the Young Talent of NEAC, and the Innovative Research Team of NEAC (2018) 98.

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Authors and Affiliations

  1. Key Laboratory of Linguistic and Cultural Computing of Ministry of Education, Chinese National Information Technology Research Institute, Northwest Minzu University, Lanzhou, Gansu, 730030, China

    Jianhua Wang, Jiacheng Li, Wenjie Liu, Shiqiang Du & Shengxia Gao

  2. College of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, 730030, Gansu, China

    Shiqiang Du

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  1. Jianhua Wang
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Correspondence to Shiqiang Du.

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Wang, J., Li, J., Liu, W. et al. Dunhuang Mural Line Drawing Based on Multi-scale Feature Fusion and Sharp Edge Learning. Neural Process Lett 55, 10201–10214 (2023). https://doi.org/10.1007/s11063-023-11323-z

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