计算机科学 ›› 2022, Vol. 49 ›› Issue (6): 217-223.doi: 10.11896/jsjkx.210500105
徐辉1,2, 康金梦1, 张加万1
XU Hui1,2, KANG Jin-meng1, ZHANG Jia-wan1
摘要： 敦煌壁画存在多种病害造成的不规则破损区域,运用数字修复的方式对其进行恢复,既不会对原始壁画造成损坏,又可以获得较好的修补效果。由于壁画修补问题中缺失的区域较大,不能用局部非语义的修补方法来实现。针对敦煌壁画缺损区域的修复问题,设计了基于生成对抗网络的图像修补方法,使用语义上合理的内容来渲染缺失区域的像素,实现非接触性壁画场景重建,从而提升壁画虚拟修复准确度。该算法在生成对抗神经网络的基础上引入感知损失函数,在生成模型中添加3层扩张卷积层来收集破损区域的图像特征,利用感知损失提升模型对高频纹理细节的修复能力,运用扩展卷积提取范围特征激励生成模型生成较高质量的图像结果。在敦煌壁画数据集上将所提方法与3种优秀方法进行了比较,修复结果显示所提算法在测试数据集上的PSNR评分提高了1.79%,SSIM评分提高了7.7%。所提修复模型提升了破损壁画的修复精度,使修复结果更加准确。
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