【摘要】 目前中华传统刺绣工艺传承保护问题中的修复任务主要以人工为主，修复过程需要大量的人力、物力。随着深度学习的高速发展，不同类型的刺绣文物损伤可以利用生成对抗网络进行修复。针对上述问题，提出一种基于改进深度卷积生成对抗网络（DCGAN）的刺绣图像修复方法。首先，在生成器部分引入空洞卷积层扩大感受野，并添加卷积注意力机制模块，在通道与空间2个维度增强重要特征的指导作用；在判别器部分增加全连接层数提升网络解决非线性问题的能力；在损失函数部分联合均方误差损失与对抗损失通过网络训练相互博弈的过程实现刺绣图像修复。实验结果表明：引入空洞卷积层与注意力机制提升了网络性能与修复效果，最终得到修复图像的结构相似性高达0.955，能够得到较为自然的刺绣图像修复效果，可以为专家提供纹理、色彩等信息作为参考辅助后续的修复。更多还原

【Abstract】 Presently, image inpainting in the inheritance and protection of Chinese traditional embroidery often depend on human labor, with considerable work force and material resources. Furthermore, with the rapid development of deep learning,generative adversarial networks can be applied to repair damaged embroidery relics. An embroidery image restoration method based on improved deep convolutional generative adversarial network(DCGAN) is proposed to solve the above problems. In the generator part, dilated convolution is introduced to expand receptive fields; the addition of the convolution attentionmechanism module enhances the guiding role of significant features in two dimensions of channel and space. In the discriminator part, the number of full connection layers are increased to improve the ability of the network to solve nonlinear problems. In the loss function part, the mean square error loss and confrontation loss are combined to realize embroidery image inpainting through the game process of network training. The experimental results show that the dilated convolution and convolution attention mechanism module improves the network performance and repair effect, and the structural similarity of the repaired image is as high as 0. 955. This method enables obtaining a more natural embroidery image-restoration effect, which can provide experts with information such as texture and color as a reference to assist subsequent repair.更多还原