针对于当前人脸遮挡修复方法中出现修复图像信息不完整、纹理模糊、产生伪影、细节欠佳以及模型训练不稳定等问题,提出一种基于CSWin-Transformer和WGAN的人脸遮挡修复方法。该方法以Encoder-Decoder结构作为生成器,在生成器中引入CSWin...针对于当前人脸遮挡修复方法中出现修复图像信息不完整、纹理模糊、产生伪影、细节欠佳以及模型训练不稳定等问题,提出一种基于CSWin-Transformer和WGAN的人脸遮挡修复方法。该方法以Encoder-Decoder结构作为生成器,在生成器中引入CSWin-Transformer Block来精细识别和处理被遮挡的面部区域,以提高处理的针对性和效率,解码器通过跳跃连接与编码器多尺度特征融合,更好学习图像的细节特征,优化最终效果。在判别器中引入Wasserstein距离,来提高模型训练稳定性以及生成图像的真实性,同时在判别器中引入CSWinSelf-Attention,增强判别器对图像全局结构和细节信息的理解。实验结果显示,文章方法在所使用的CelebA的数据集上有良好的修复效果,在峰值信噪比(PSNR)和结构相似性指数(SSIM)指标上与目前一些图像修复方法相比表现更优。 In view of the problems of incomplete repair image information, blurred texture, artifacts, poor details and unstable model training, a face occlusion repair method based on CSWin-Transformer and WGAN is proposed. This method takes Encoder-Decoder, structure as the generator, and introduces CSWin-Transformer Block in the generator to finely identify and process the occluded face areas, so as to improve the pertinacity and efficiency of processing. The decoder integrates with the encoder multi-scale features through jump connection to better learn the detailed features of the image and optimize the final effect. The Wasserstein distance is introduced into the discriminator to improve the stability of the model training and the authenticity of the generated image. Meanwhile, CSWin Self-Attention is introduced in the discriminator to enhance the understanding of the global structure and details of the image. The experimental results show that the method has good repair effect on the data set of CelebA used, and better than some current image repair methods in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) indicators.展开更多
文摘针对于当前人脸遮挡修复方法中出现修复图像信息不完整、纹理模糊、产生伪影、细节欠佳以及模型训练不稳定等问题,提出一种基于CSWin-Transformer和WGAN的人脸遮挡修复方法。该方法以Encoder-Decoder结构作为生成器,在生成器中引入CSWin-Transformer Block来精细识别和处理被遮挡的面部区域,以提高处理的针对性和效率,解码器通过跳跃连接与编码器多尺度特征融合,更好学习图像的细节特征,优化最终效果。在判别器中引入Wasserstein距离,来提高模型训练稳定性以及生成图像的真实性,同时在判别器中引入CSWinSelf-Attention,增强判别器对图像全局结构和细节信息的理解。实验结果显示,文章方法在所使用的CelebA的数据集上有良好的修复效果,在峰值信噪比(PSNR)和结构相似性指数(SSIM)指标上与目前一些图像修复方法相比表现更优。 In view of the problems of incomplete repair image information, blurred texture, artifacts, poor details and unstable model training, a face occlusion repair method based on CSWin-Transformer and WGAN is proposed. This method takes Encoder-Decoder, structure as the generator, and introduces CSWin-Transformer Block in the generator to finely identify and process the occluded face areas, so as to improve the pertinacity and efficiency of processing. The decoder integrates with the encoder multi-scale features through jump connection to better learn the detailed features of the image and optimize the final effect. The Wasserstein distance is introduced into the discriminator to improve the stability of the model training and the authenticity of the generated image. Meanwhile, CSWin Self-Attention is introduced in the discriminator to enhance the understanding of the global structure and details of the image. The experimental results show that the method has good repair effect on the data set of CelebA used, and better than some current image repair methods in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) indicators.
