Single-Pixel Imaging Reconstruction Network with Hybrid Attention and Enhanced U-Net
Single-pixel imaging has the characteristics of a simple structure and low cost, which means it has potential applications in many fields. This paper proposes an image reconstruction method for single-pixel imaging (SPI) based on deep learning. This method takes the Generative Adversarial Network (G...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/12/6/607 |
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| author | Bingrui Xiao Huibin Wang Yang Bu |
| author_facet | Bingrui Xiao Huibin Wang Yang Bu |
| author_sort | Bingrui Xiao |
| collection | DOAJ |
| description | Single-pixel imaging has the characteristics of a simple structure and low cost, which means it has potential applications in many fields. This paper proposes an image reconstruction method for single-pixel imaging (SPI) based on deep learning. This method takes the Generative Adversarial Network (GAN) as the basic architecture, combines the dense residual structure and the deep separable attention mechanism, and reduces the parameters while ensuring the diversity of feature extraction. It also reduces the amount of computation and improves the computational efficiency. In addition, dual-skip connections between the encoder and decoder parts are used to combine the original detailed information with the overall information processed by the network structure. This approach enables a more comprehensive and efficient reconstruction of the target image. Both simulations and experiments have confirmed that the proposed method can effectively reconstruct images at low sampling rates and also achieve good reconstruction results on natural images not seen during training, demonstrating a strong generalization capability. |
| format | Article |
| id | doaj-art-e4d2eb1f8b0745a69ec234e6e583ec4f |
| institution | Kabale University |
| issn | 2304-6732 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj-art-e4d2eb1f8b0745a69ec234e6e583ec4f2025-08-20T03:29:39ZengMDPI AGPhotonics2304-67322025-06-0112660710.3390/photonics12060607Single-Pixel Imaging Reconstruction Network with Hybrid Attention and Enhanced U-NetBingrui Xiao0Huibin Wang1Yang Bu2College of Computer and Information, Hohai University, Nanjing 211100, ChinaCollege of Computer and Information, Hohai University, Nanjing 211100, ChinaShanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, ChinaSingle-pixel imaging has the characteristics of a simple structure and low cost, which means it has potential applications in many fields. This paper proposes an image reconstruction method for single-pixel imaging (SPI) based on deep learning. This method takes the Generative Adversarial Network (GAN) as the basic architecture, combines the dense residual structure and the deep separable attention mechanism, and reduces the parameters while ensuring the diversity of feature extraction. It also reduces the amount of computation and improves the computational efficiency. In addition, dual-skip connections between the encoder and decoder parts are used to combine the original detailed information with the overall information processed by the network structure. This approach enables a more comprehensive and efficient reconstruction of the target image. Both simulations and experiments have confirmed that the proposed method can effectively reconstruct images at low sampling rates and also achieve good reconstruction results on natural images not seen during training, demonstrating a strong generalization capability.https://www.mdpi.com/2304-6732/12/6/607single-pixel imaginggenerative adversarial networkimage reconstructiondeep learning |
| spellingShingle | Bingrui Xiao Huibin Wang Yang Bu Single-Pixel Imaging Reconstruction Network with Hybrid Attention and Enhanced U-Net Photonics single-pixel imaging generative adversarial network image reconstruction deep learning |
| title | Single-Pixel Imaging Reconstruction Network with Hybrid Attention and Enhanced U-Net |
| title_full | Single-Pixel Imaging Reconstruction Network with Hybrid Attention and Enhanced U-Net |
| title_fullStr | Single-Pixel Imaging Reconstruction Network with Hybrid Attention and Enhanced U-Net |
| title_full_unstemmed | Single-Pixel Imaging Reconstruction Network with Hybrid Attention and Enhanced U-Net |
| title_short | Single-Pixel Imaging Reconstruction Network with Hybrid Attention and Enhanced U-Net |
| title_sort | single pixel imaging reconstruction network with hybrid attention and enhanced u net |
| topic | single-pixel imaging generative adversarial network image reconstruction deep learning |
| url | https://www.mdpi.com/2304-6732/12/6/607 |
| work_keys_str_mv | AT bingruixiao singlepixelimagingreconstructionnetworkwithhybridattentionandenhancedunet AT huibinwang singlepixelimagingreconstructionnetworkwithhybridattentionandenhancedunet AT yangbu singlepixelimagingreconstructionnetworkwithhybridattentionandenhancedunet |