基于变分自编码器的人脸图像修复

基于卷积神经网络的人脸图像修复技术在刑事侦破、文物保护及影视特效等领域有着重要的应用.但现有方法存在着图像修复结果不够清晰以及结果多样化不足等缺点,为此,提出了一种基于变分自编码器的人脸图像修复方法.首先设计了一种变分自编码器的变种网络,通过引入生成对抗网络解决修复人脸图像不清晰的问题,同时对变分自编码器中的隐变量进行约束,使得其中各个维度相互独立,实现特征解耦操作;最后通过动态规划获得最佳分割边界,利用泊松图像编辑得到无缝融合的结果.在CelebA数据集上的实验结果表明,该方法获得了良好的图像修复结果,同时,通过显式地控制隐变量的不同维度,展现了不同属性的人脸图像修复结果.

Face Image Inpainting via Variational Autoencoder

Face image inpainting based on the convolutional neural network has enabled a variety of applications, ranging from criminal investigation to cultural relics protection. However, the results of existing methods are often limited to insufficient diversity and still far from realistic. In this work, we generate more reasonable missing facial content with a variant of variational auto-encoder, as well as generative adversarial network. Furthermore, we impose constraints on latent variables to encourage the distribution of representations to be factorial that making them independent across dimensions. Latter, the optimal boundary was obtained through dynamic programming, and finally we get the seamless results by Poisson image editing. Experiments on CelebA dataset demonstrated that the proposed method achieved better inpainting results and disentanglement.