基于谱归一化条件生成对抗网络的图像修复算法

基于生成对抗网络的图像修复算法在修复大尺寸缺失图像时,存在图像失真较多与判别网络性能不可控等问题,基于谱归一化条件生成对抗网络,提出一种新的图像修复算法。引入谱归一化来约束判别网络的判别性能,间接提高修复网络的修复能力,并根据控制判别网络性能对谱归一化进行理论分析。通过类别信息约束特征生成,保证修复图像的内容不变性,引入扩展卷积算子对待修复图像进行像素级操作,解决修复图像缺乏局部一致性的问题。在此基础上,运用PSNR、SSIM等图像评价方法及分片Wasserstein距离、Inception分数、流形距离度量、GAN-train和GAN-test等流形结构相似度评价指标对修复图像进行综合评价。实验结果表明,与CE、GL等算法相比,该算法获得的修复图像在主观感受和客观评价指标上均有明显提高。

Image Inpainting Algorithm Based on Conditional Generative Adversarial Network with Spectral Normalization

To solve the problem of large image distortion and uncontrollable discriminative network performance in image inpainting based on Generative Adversarial Network(GAN),this paper proposes a new image inpainting algorithm based on conditional generative adversarial network with spectral normalization.Spectral normalization is introduced to constrain the discriminative performance of discriminative network,and thus bring an improvement to the inpainting network performance followed by detailed theoretical analysis of spectral normalization in controlling the discriminative network performance.Category information is used to constrain feature generation to ensure that content of the inpainted image is close to that of the original image.The extended convolution operator is also introduced to perform pixel-level operation on the tobe-inpainted image to address the lack of local consistency in image inpainting.On this basis,PSNR,SSIM and other image evaluation methods,as well as slice Wasserstein distance,Inception score,manifold distance measurement,GAN-train,GANtest and other manifold structure similarity evaluation indicators are used to comprehensively evaluate the inpainted image.Experimental results show that compared with CE,GL and other algorithms,the proposed algorithm can significantly improve the subjective and objective evaluation indicators of the inpainted images.