基于孪生级联空间滤波的中国传统画修复  

Restoration of traditional Chinese painting based on twin cascade spatial filtering

作　　者：薛文喆 董兴宇 胡琦瑶 曹瑞 彭先霖 XUE Wenzhe;DONG Xingyu;HU Qiyao;CAO Rui;PENG Xianlin(School of Information Science and Technology,Northwest University,Xi’an 710127,China;School of Art,Northwest University,Xi’an 710127,China)

机构地区：[1]西北大学信息科学与技术学院,陕西西安710127 [2]西北大学艺术学院,陕西西安710127

出　　处：《西北大学学报(自然科学版)》2025年第1期150-167,共18页Journal of Northwest University(Natural Science Edition)

基　　金：国家自然科学基金(62471390,62306237);陕西省重点研发计划(2024GX-YBXM-149);西北大学研究生创新项目(CX2024204、CX2024206)。

摘　　要：中国传统画作为宝贵的文化遗产,历经时间沉淀以及各种自然因素的影响,常出现开裂、破损和褪色等问题。尽管一些深度学习框架在自然图像修复领域取得了显著进展,但其大多过度依赖卷积权重共享和平移不变性,在处理布局复杂、结构抽象的绘画图像时,难以捕捉其独特的空间特性。针对此问题,提出一种孪生级联空间滤波(twin cascade spatial filtering,TCSF)预测方法用于中国传统画的修复。TCSF采用层级解码策略,从多尺度解析绘画图像的层次特征,并级联空间滤波预测方法得到修复核,从而由粗到细地复原缺失区域的像素。为了在特征信息匮乏的区域精确地复原缺失的结构和笔触信息,进一步引入空间编码机制。通过对滤波特征图空间编码得到坐标矩阵,并在滤波预测过程中注入坐标信息编码,用于缺失像素点恢复时提供空间信息参照,进而提升修复结果的精确度与视觉效果。实验中,选取了具有代表性的中国传统画图像进行训练,并增加壁画数据集和Places数据集测试模型的泛化性能。与现有工作使用掩码不同,该研究在实验中提取部分真实绘画图像的破损掩码,以更逼真地模拟破损情况。定性和定量实验结果表明,该方法在中国传统画恢复任务中取得了较好的修复结果,为数字艺术修复和文化遗产保护提供了有益的启示。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.

关 键 词：图像修复 空间滤波预测 中国传统画修复 文物图像破损掩码 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术]