Bai, X; Yang, Y; Yang, W; Zhu, R; Xue, JH; (2025) Identity-Preserving Diffusion for Face Restoration. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. IEEE: Hyderabad, India.

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Abstract

Face restoration is a critical task in computer vision, aiming to restore high-quality facial images from degraded inputs. In existing diffusion models, identity information is not well preserved when confronted with severely degradation. To address this challenge, we propose a Local Patch-Based Identity-Preserving Diffusion (LPIP-Diff) framework. Our local patch-based strategy leverages the interrelationships between neighboring patches to model highly structured facial context, which facilitates the restoration of fine-grained details and the preservation of identity-related features. We also introduce a fusion degradation estimation method that makes each overlapping area restored multiple times by adjacent patches, effectively restoring local details. The experimental results of LPIP-Diff on three publicly available datasets, including one severely degraded dataset, consistently demonstrate its superiority over the state-of-the-art methods in terms of both quantitative and qualitative evaluations, strikes a good balance between realism and fidelity, and enhances robustness against degradation.

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