Enhanced Facial Restoration with Misinformation-Filtered Guide-Denoising Diffusion Probabilistic Models

Most of the existing generation models encounter notable challenges in complex scenes, particularly with inaccuracies in facial organs and textures that do not align with actual conditions. Traditional face restoration methods, heavily dependent on facial geometry and reference priors, often generate incorrect facial images that contribute misleading prior information. In this study, a Misinformation-Flitered GuideDenoising Diffusion Probabilistic Models (MF-GDDPM) is proposed to address these issue. Specifically, MF-GDDPM employs low-pass filtering to remove high-frequency details that contain misleading prior information. This process results in filtered low-dimensional facial contours that guide the diffusion model in generating high-quality facial images. To further enhance the fidelity of the generated results, a dualstream encoder within the Denoising Unet is constructed to process facial contours and high-dimensional details separately, while the Attention Feature Fusion (AFF) attention mechanism ensures the fidelity of image restoration. We have also incorporated the Natural Image Quality Evaluator (NIQE), a deep learning-based image quality assessment tool, into our framework as a novel loss function to crucially ensure the naturalness of restored images. Overall, the proposed method marks a significant improvement in generating accurate and clear facial images using diffusion models.