UFSRNet: U-shaped face super-resolution reconstruction network based on wavelet transform

Wang, T; Xiao, Y; Cai, Y; Gao, G; Jin, X; Wang, L; Lai, H

UFSRNet: U-shaped face super-resolution reconstruction network based on wavelet transform

Wang, T Xiao, Y

Cai, Y Gao, G Jin, X Wang, L Lai, H

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2024, 83, (25), pp. 67231-67249
Issue Date:: 2024-07-01

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Field	Value	Language
dc.contributor.author	Wang, T
dc.contributor.author	Xiao, Y https://orcid.org/0000-0002-5864-1957
dc.contributor.author	Cai, Y
dc.contributor.author	Gao, G
dc.contributor.author	Jin, X
dc.contributor.author	Wang, L
dc.contributor.author	Lai, H
dc.date.accessioned	2025-03-17T23:07:50Z
dc.date.available	2025-03-17T23:07:50Z
dc.date.issued	2024-07-01
dc.identifier.citation	Multimedia Tools and Applications, 2024, 83, (25), pp. 67231-67249
dc.identifier.issn	1380-7501
dc.identifier.issn	1573-7721
dc.identifier.uri	http://hdl.handle.net/10453/185942
dc.description.abstract	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.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Multimedia Tools and Applications
dc.relation.isbasedon	10.1007/s11042-024-18284-y
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	Software Engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4605 Data management and data science
dc.subject.classification	4606 Distributed computing and systems software
dc.title	UFSRNet: U-shaped face super-resolution reconstruction network based on wavelet transform
dc.type	Journal Article
utslib.citation.volume	83
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2025-03-17T23:07:47Z
pubs.issue	25
pubs.publication-status	Published
pubs.volume	83
utslib.citation.issue	25

Abstract:

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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http://hdl.handle.net/10453/185942