UFSRNet: U-shaped face super-resolution reconstruction network based on wavelet transform
Multimedia Tools and Applications(2024)
摘要
Aiming to address the issues of excessive parameters and inadequate recovery of facial details in certain existing face super-resolution networks, we propose a U-shaped face super resolution reconstruction network based on wavelet transform. Firstly, a novel Refined Feature Extraction Block (RFEB) is proposed in the Down-sampling Unit, which uses two depth-separable convolution blocks as the main branch and introduces a feature calibration path branch and a residual branch to perform refined feature extraction of the original face images. Secondly, in order to further reduce the number of network parameters, a novel Double Branch Distillation Fusion Block (DBDFB) is designed, which uses two branches to process the features extracted in the down-sampling stage respectively. Finally, Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) are used to extract and retain high-frequency detail information of face images. Quantitative and qualitative experiments show that our method outperforms state-of-the-art face super-resolution algorithms using only a few parameters. The source codes of the proposed method are available at https://github.com/Aichiniuroumian/UFSRNet .
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关键词
Face super-resolution,Wavelet transform,Residual network,Unet
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