结构引导的渐进式生成对抗壁画修复

Mural inpainting progressive generative adversarial networks based on structure guided

针对破损壁画图像修复过程中存在的结构修复不当及修复后壁画细节重构丢失等问题,提出了一种基于结构引导的渐进式生成对抗壁画修复深度学习模型。设计结构生成器对壁画缺失结构内容进行修复,得到修复的壁画结构图像。通过壁画生成器生成对抗学习,结合改进的双池化SKNet多尺度特征提取模块,利用修复后的结构图像引导破损壁画实现渐进式修复,以提高壁画的细节特征学习能力。通过局部判别器和全局判别器,完成对结构图像和壁画图像的重构判别,增强壁画修复效果的全局一致性。通过对真实敦煌壁画数字化修复的实验表明:所提方法能够有效修复破损的敦煌壁画,修复后的壁画具有更好的结构及细节信息,在主客观评价指标上均优于比较方法。

Aiming at the problems of improper structural repair and loss of mural detail reconstruction after repairing during the process of damaged mural image inpainting,mural inpainting progressive generative adversarial networks based on structure guided is proposed.Firstly,a structure generator is designed to generate the missing structure content of the mural.Secondly,the mural generator is used to generate adversarial learning,and combined with the improved double pooling SKNet multi-scale feature extraction modular,the repaired structure image is used to guide the damaged mural to achieve progressive repair,which improves the detailed feature learning ability of the mural.Lastly,the reconstruction of the structural picture and the mural image is finished using the local discriminator and the global discriminator,which improves the overall consistency of the mural restoration result.Experiments on digital restoration of real Dunhuang murals show that the proposed method can effectively repair damaged Dunhuang murals,and the restored murals have a stronger structure and high-quality texture details than other comparison algorithms.Meanwhile,the proposed has better both subjective and objective evaluation.