@article{XUE2025, 
author = {Wenzhe XUE and Xingyu DONG and Qiyao HU and Rui CAO and Xianlin PENG},
title = {Restoration of traditional Chinese painting based on twin cascade spatial filtering},
year = {2025},
journal = {Journal of Northwest University (Natural Science Edition)},
volume = {55},
number = {1},
pages = {150-167},
keywords = {image restoration, spatial filter prediction, restoration of traditional Chinese paintings, broken images of cultural relics mask},
url = {https://www.sciopen.com/article/10.16152/j.cnki.xdxbzr.2025-01-013},
doi = {10.16152/j.cnki.xdxbzr.2025-01-013},
abstract = {Traditional Chinese paintings are invaluable cultural legacies, but they often suffer from issues such as cracking, damage, and fading due to the effects of time and various natural factors. While some deep learning frameworks have made significant progress in natural image restoration, they tend to rely heavily on convolutional weight sharing and translational invariance. This reliance may limit their ability to fully capture the unique spatial characteristics of paintings with intricate layouts and abstract structural information. To address this issue, this paper proposes a Twin Cascade Spatial Filtering (TCSF) prediction method for the restoration of traditional Chinese paintings. The TCSF adopts a hierarchical decoding strategy that analyzes the hierarchical features of painting images across multiple scales. It cascades a spatial filtering prediction approach to obtain restoration kernels, restoring missing region pixels from coarse to fine detail. Furthermore, in order to precisely restore the missing structural and brushstroke information in areas where feature information is sparse, this paper introduces a spatial encoding mechanism. By spatially encoding the filter feature maps into coordinate matrices and infusing coordinate information encoding into the filtering prediction process, this paper provides spatial reference information for the recovery of missing pixels, thereby enhancing the accuracy and visual quality of the restoration outcomes. In the experiments, the model was trained using representative images of traditional Chinese paintings, and the mural datasets and Places datasets were added to test the model’s generalization ability. In contrast to existing work that utilizes synthetic masks, this paper extracted actual damage masks from real painting images in order to more realistically simulate damage scenarios. The qualitative and quantitative experimental results demonstrate that the proposed method achieves favorable restoration results in traditional Chinese painting recovery tasks and provides useful insights for digital art restoration and cultural heritage protection.}
}