A face inpainting model based on the denoising diffusion probability model is proposed aiming at the problems of poor image quality,blurred repair edges,complex model,and difficult training of the mainstream face inpainting model after image inpainting.By improving the denoising diffusion probability model,the U-Net network structure in Guided-diffusion is adopted.The fast Fourier convolution is introduced into the network,and then the model is trained and tested on the CelebA-HQ high-definition face image dataset.The experimental results show that the improved denoising diffusion probability model can achieve a PSNR of 25.01 and a SSIM of 0.886 compared to the original image,when inpainting face images with random mask,both of which are better than the model before improvement and the existing face image inpainting model based on generative adversarial networks.
关键词
深度学习/人脸图像修复/去噪扩散概率模型/快速傅里叶卷积/U-Net网络
Key words
deep learning/face inpainting/denoising diffusion probability models/fast Fourier convolution/U-Net network
维普
万方数据