Self-Supervised Selective-Guided Diffusion Model for Old-Photo Face Restoration

• URL: http://arxiv.org/abs/2510.12114v1
• Date: Tue, 14 Oct 2025 03:34:15 GMT
• Title: Self-Supervised Selective-Guided Diffusion Model for Old-Photo Face Restoration
• Authors: Wenjie Li, Xiangyi Wang, Heng Guo, Guangwei Gao, Zhanyu Ma,
• Abstract summary: Old-photo face restoration poses significant challenges due to compounded degradations such as breakage, fading, and severe blur.<n>We propose Self-Supervised Selective-Guided Diffusion, which leverages pseudo-reference faces generated by a pre-trained diffusion model under weak guidance.<n>SSDiff outperforms existing GAN-based and diffusion-based methods in perceptual quality, fidelity, and regional controllability.
• Score: 43.12011252251526
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Old-photo face restoration poses significant challenges due to compounded degradations such as breakage, fading, and severe blur. Existing pre-trained diffusion-guided methods either rely on explicit degradation priors or global statistical guidance, which struggle with localized artifacts or face color. We propose Self-Supervised Selective-Guided Diffusion (SSDiff), which leverages pseudo-reference faces generated by a pre-trained diffusion model under weak guidance. These pseudo-labels exhibit structurally aligned contours and natural colors, enabling region-specific restoration via staged supervision: structural guidance applied throughout the denoising process and color refinement in later steps, aligned with the coarse-to-fine nature of diffusion. By incorporating face parsing maps and scratch masks, our method selectively restores breakage regions while avoiding identity mismatch. We further construct VintageFace, a 300-image benchmark of real old face photos with varying degradation levels. SSDiff outperforms existing GAN-based and diffusion-based methods in perceptual quality, fidelity, and regional controllability. Code link: https://github.com/PRIS-CV/SSDiff.

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