Super-Resolution Reconstruction Method of Face Image Based on Attention Mechanism
In recent years, convolutional neural network in Single image super-resolution field show good results. Deep networks can establish complex mapping between low-resolution and high-resolution images, making the reconstructed images quality a great progress over traditional methods. In order to be abl...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9395111/ |
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| author | Chenglin Yu Hailong Pei |
| author_facet | Chenglin Yu Hailong Pei |
| author_sort | Chenglin Yu |
| collection | DOAJ |
| description | In recent years, convolutional neural network in Single image super-resolution field show good results. Deep networks can establish complex mapping between low-resolution and high-resolution images, making the reconstructed images quality a great progress over traditional methods. In order to be able to generate face images with rich texture details, the algorithm proposed in this paper captures implicit weight information in channel and space domains through dual attention modules, so as to allocate computing resources more effectively and speed up the network convergence. Fusion of global features through residual connections in this network not only focus on learning the high frequency information of images that has been lost, but also accelerate the network convergence through effective feature supervision. In order to alleviate the defects of MAE loss function, a special Huber loss function is introduced in the algorithm. The experimental results on benchmark show that the proposed algorithm has a significant improvement in image reconstruction accuracy compared with existed SISR methods. |
| format | Article |
| id | doaj-art-ec746b2acb454d58ad07746f994fee01 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ec746b2acb454d58ad07746f994fee012025-08-20T02:40:43ZengIEEEIEEE Access2169-35362025-01-011312125012126010.1109/ACCESS.2021.30708989395111Super-Resolution Reconstruction Method of Face Image Based on Attention MechanismChenglin Yu0https://orcid.org/0000-0002-4413-3142Hailong Pei1https://orcid.org/0000-0003-3295-4557Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, South China University of Technology, Guangzhou, ChinaKey Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, School of Automation Science and Engineering, South China University of Technology, Guangzhou, ChinaIn recent years, convolutional neural network in Single image super-resolution field show good results. Deep networks can establish complex mapping between low-resolution and high-resolution images, making the reconstructed images quality a great progress over traditional methods. In order to be able to generate face images with rich texture details, the algorithm proposed in this paper captures implicit weight information in channel and space domains through dual attention modules, so as to allocate computing resources more effectively and speed up the network convergence. Fusion of global features through residual connections in this network not only focus on learning the high frequency information of images that has been lost, but also accelerate the network convergence through effective feature supervision. In order to alleviate the defects of MAE loss function, a special Huber loss function is introduced in the algorithm. The experimental results on benchmark show that the proposed algorithm has a significant improvement in image reconstruction accuracy compared with existed SISR methods.https://ieeexplore.ieee.org/document/9395111/Super-resolutionfeature supervisionchannel attentionspatial attention |
| spellingShingle | Chenglin Yu Hailong Pei Super-Resolution Reconstruction Method of Face Image Based on Attention Mechanism IEEE Access Super-resolution feature supervision channel attention spatial attention |
| title | Super-Resolution Reconstruction Method of Face Image Based on Attention Mechanism |
| title_full | Super-Resolution Reconstruction Method of Face Image Based on Attention Mechanism |
| title_fullStr | Super-Resolution Reconstruction Method of Face Image Based on Attention Mechanism |
| title_full_unstemmed | Super-Resolution Reconstruction Method of Face Image Based on Attention Mechanism |
| title_short | Super-Resolution Reconstruction Method of Face Image Based on Attention Mechanism |
| title_sort | super resolution reconstruction method of face image based on attention mechanism |
| topic | Super-resolution feature supervision channel attention spatial attention |
| url | https://ieeexplore.ieee.org/document/9395111/ |
| work_keys_str_mv | AT chenglinyu superresolutionreconstructionmethodoffaceimagebasedonattentionmechanism AT hailongpei superresolutionreconstructionmethodoffaceimagebasedonattentionmechanism |