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[bibtex]@InProceedings{Miao_2024_CVPR, author = {Miao, Yunqi and Deng, Jiankang and Han, Jungong}, title = {WaveFace: Authentic Face Restoration with Efficient Frequency Recovery}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {6583-6592} }
WaveFace: Authentic Face Restoration with Efficient Frequency Recovery
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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