An Improved Face Image Restoration Method Based on Denoising Diffusion Probabilistic Models

Image restoration is a crucial task in computer vision, aiming to fill in missing areas within an image to restore its integrity. Traditional methods fall short when dealing with intricate facial image restoration, often failing to produce high-quality results. Denoising Diffusion Probabilistic Models(DDPM), characterized by its diversity and stability, plays a significant role in the domain of facial image restoration. This study aims to explore a facial image restoration method based on DDPM, utilizing a pre-trained unconditional DDPM model to achieve more flexible facial image restoration. At the same time, this study found that when the total number of iterations in the resampling process is relatively low, the quality of the restored image is poor. Therefore, we propose a method to optimize the inversion process by combining progressive sampling with sample scheduling to improve the quality of the restored images, and conduct extensive experiments on the CelebA-HQ and FFHQ datasets. Comparisons with other methods demonstrate that our approach yields higher-quality results in facial image restoration. Our method achieved the best results in terms of PSNR and LPIPS metrics. For random masks, the accuracy of face recognition increased by 15.7% after the restoration of facial images. For central masks, the accuracy improved by 26%.