SCGAN: Generative Adversarial Networks of Skip Connection for Face Image Inpainting

Yuhang Zhang, Q. Zhang, Man Jiang, Jiangtao Su
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Abstract

Deep learning has been widely applied for jobs involving face inpainting, however, there are usually some problems, such as incoherent inpainting edges, lack of diversity of generated images and other problems. In order to get more feature information and improve the inpainting effect, we therefore propose a Generative Adversarial Network of Skip Connection (SCGAN), which connects the encoder layers and the decoder layers by skip connection in the generator. The coherence and consistency of the image inpainting edges are improved, and the finer features of the image inpainting are refined, simultaneously using the discriminator's local and global double discriminators model. We also employ WGAN-GP loss to enhance model stability during training, prevent model collapse, and increase the variety of inpainting face images. Finally, experiments on the CelebA dataset and the LFW dataset are performed, and the model's performance is assessed using the PSNR and SSIM indices. Our model's face image inpainting is more realistic and coherent than that of other models, and the model training is more reliable.
SCGAN:基于跳跃连接的人脸图像绘制生成对抗网络
深度学习已经被广泛应用于人脸图像绘制的工作中,但通常存在一些问题,如绘制边缘不连贯、生成的图像缺乏多样性等问题。为了获得更多的特征信息,提高图像的绘制效果,我们提出了一种生成对抗网络的跳跃连接(SCGAN),该网络在生成器中通过跳跃连接连接编码器层和解码器层。同时利用鉴别器的局部和全局双鉴别器模型,提高了图像补图边缘的连贯性和一致性,细化了图像补图的精细特征。我们还利用WGAN-GP损失来增强模型在训练过程中的稳定性,防止模型崩溃,并增加面部图像的多样性。最后,在CelebA数据集和LFW数据集上进行了实验,并使用PSNR和SSIM指标对模型的性能进行了评估。我们的模型绘制的人脸图像比其他模型更真实、连贯,模型训练更可靠。
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