WaveFace: Authentic Face Restoration with Efficient Frequency Recovery

• URL: http://arxiv.org/abs/2403.12760v1
• Date: Tue, 19 Mar 2024 14:27:24 GMT
• Title: WaveFace: Authentic Face Restoration with Efficient Frequency Recovery
• Authors: Yunqi Miao, Jiankang Deng, Jungong Han,
• Abstract summary: diffusion models are criticized for two problems: 1) slow training and inference speed, and 2) failure in preserving identity and recovering fine-grained facial details. We propose WaveFace to solve the problems in the frequency domain, where low- and high-frequency components decomposed by wavelet transformation are considered individually. We show that WaveFace outperforms state-of-the-art methods in authenticity, especially in terms of identity preservation.
• Score: 74.73492472409447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although diffusion models are rising as a powerful solution for blind face restoration, they are criticized for two problems: 1) slow training and inference speed, and 2) failure in preserving identity and recovering fine-grained facial details. In this work, we propose WaveFace to solve the problems in the frequency domain, where low- and high-frequency components decomposed by wavelet transformation are considered individually to maximize authenticity as well as efficiency. The diffusion model is applied to recover the low-frequency component only, which presents general information of the original image but 1/16 in size. To preserve the original identity, the generation is conditioned on the low-frequency component of low-quality images at each denoising step. Meanwhile, high-frequency components at multiple decomposition levels are handled by a unified network, which recovers complex facial details in a single step. Evaluations on four benchmark datasets show that: 1) WaveFace outperforms state-of-the-art methods in authenticity, especially in terms of identity preservation, and 2) authentic images are restored with the efficiency 10x faster than existing diffusion model-based BFR methods.

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