Abstract
Dunhuang murals are an excellent cultural heritage, a masterpiece of Chinese painting, and a treasure of Buddhist art. A large number of murals and sculptures have gone through thousands of years and are of high artistic value. Digital line drawings of the Dun- huang Murals can not only show the beauty of the line art of murals, but also guide the restoration of murals. It belongs to the direction of image edge detection in computer vision. The purpose of edge detection is to quickly and accurately locate and extract image edge feature information. Although some traditional detection algorithms and methods based on deep learning have made some progress, they have not achieved ideal results in generating mural datasets. Compared to existing methods, we propose a novel edge detection architecture Bi-Dexined network. Firstly, the adaptive weight in this method can well balance the weight influence of the fusion of different levels of feature maps on the final prediction result. Secondly, The upsampling path can extract deeper semantic information in the network. After testing on several different edge detection and mural datasets, our method can generate clearer and more reasonable edge maps than other methods.
The first author is a student.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China (No. 61866033), the Outstanding Graduate “Innovation Star” of Gansu Province (No. 2022CXZX-202), the Introduction of Talent Research Project of Northwest Minzu University (No. xbmuyjrc201904), and the Fundamental Research Funds for the Central Universities of Northwest Minzu University (No. 31920220019, 31920220130), and the Leading Talent of National Ethnic Affairs Commission (NEAC), the Young Talent of NEAC, and the Innovative Research Team of NEAC (2018) 98.
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Liu, B., Du, S., Li, J., Wang, J., Liu, W. (2022). Dunhuang Mural Line Drawing Based on Bi-Dexined Network and Adaptive Weight Learning. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13534. Springer, Cham. https://doi.org/10.1007/978-3-031-18907-4_22
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