Year: 2026
Type: article
Abstract: Chinese Haipai New Year paintings are an important part of the country’s intangible cultural heritage, and their digital preservation holds great significance. This paper proposes PSE-Net (Pyramid Scale Expansion Network), a deep learning-based segmentation method specifically designed to handle the complex textures and intricate compositions of these artworks. By constructing a dedicated large-scale dataset, we trained PSE-Net to achieve high-precision segmentation by incorporating attention mechanisms and multi-scale feature fusion to better capture detailed features. Experimental results demonstrate that the proposed method outperforms existing approaches (such as ResNet) in terms of segmentation performance, yielding superior results in edge preservation. This work establishes the first automated tool for the pixel-level analysis of Haipai New Year paintings, thereby facilitating museum digitization, art history research, and education. Furthermore, it offers new insights for the image processing and digital preservation of other traditional artworks.
Source: Information
DOI: 10.3390/info17040392
Authors: Yueyang Zhao, Jingru Zhang, Jin Liu, Damin Ding
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7155068128