基于生成对抗网络的人脸图像修复

Face Image Restoration Based on Generating Confrontation Network

为了有效修复大面积破损的面部图像,通过生成对抗网络,使用解码器-编码器结构的卷积神经网络作为生成模型,增强生成模型的结构信息预测能力,同时引入对抗训练策略优化生成模型。该模型首先训练一个判别模型识别真实图像,再将判别待修复图像输入生成模型后所得到的结果是否真实,从此为生成模型提供优化梯度。结合卷积神经网络的结构信息预测能力和DCGAN对抗策略的优化能力,提高图像补全效果。通过在CeleBA人脸数据集上进行的实验结果表明:该方法在补全大面积破损的图像任务上性能明显优于其他方法。

In order to effectively repair large-area damaged facial images, a convolutional neural network with a decoder-encoder structure is used as a generation model to enhance the structural information prediction ability of the generated model, introducing a confrontation training strategy optimization generation at the same time. The model first trains a discriminant model to identify the real image, which can be used to determine whether the output obtained is realistic or not, and thus provides an optimized gradient for the generated model. Combining the structural information prediction ability of the convolutional neural network and the optimization ability of the DCGAN countermeasure strategy, the image completion effect is improved. The experimental results on the CeleBA face dataset show that the proposed method is superior to other methods in complementing large-area broken image tasks.