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	BFRFormer: Transformer-based generator for Real-World Blind Face Restoration
	Guojing Ge2,5; Qi Song6; Guibo Zhu2,3,4,5; Yuting Zhang1; Jinglu Chen2; Miao Xin2; Ming Tang2; Jinqiao Wang2,3,5
	2024-04-14
会议日期	2024年4月14日到2024年4月19日
会议地点	Seoul, Korea
英文摘要	Blind face restoration is a challenging task due to the unknown and complex degradation. Although face prior-based methods and reference-based methods have recently demonstrated high-quality results, the restored images tend to contain over-smoothed results and lose identity-preserved details when the degradation is severe. It is observed that this is attributed to short-range dependencies, the intrinsic limitation of convolutional neural networks. To model long-range dependencies, we propose a Transformer-based blind face restoration method, named BFRFormer, to reconstruct images with more identity-preserved details in an end-to-end manner. In BFRFormer, to remove blocking artifacts, the wavelet discriminator and aggregated attention module are developed, and spectral normalization and balanced consistency regulation are adaptively applied to address the training instability and over-fitting problem, respectively. Extensive experiments show that our method outperforms state-of-the-art methods on a synthetic dataset and four real-world datasets. The source code, Casia-Test dataset, and pre-trained models is released at https://github.com/s8Znk/BFRFormer.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/57281]
专题	紫东太初大模型研究中心
作者单位	1.China Telecom Corporation Ltd 2.Institute of Automation, Chinese Academy of Sciences 3.University of Chinese Academy of Sciences 4.Shanghai Artificial Intelligence Laboratory 5.Wuhan AI Research 6.Hong Kong Baptist University
推荐引用方式 GB/T 7714	Guojing Ge,Qi Song,Guibo Zhu,et al. BFRFormer: Transformer-based generator for Real-World Blind Face Restoration[C]. 见:. Seoul, Korea. 2024年4月14日到2024年4月19日.

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