City Research Online

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.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	For the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) license to any Accepted Manuscript version arising.
Publisher Keywords:	Diffusion model, face restoration, identity preservation, local patches
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Departments:	Bayes Business School Bayes Business School > Faculty of Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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