Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network
Single-pixel imaging (SPI), an imaging technique based on the theory of compressed sensing, is limited in real-time imaging and high-resolution images due to its relatively slow imaging speed. In recent years, deep unfolding network compressed sensing reconstruction algorithms based on deep learning...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10577085/ |
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| author | Quan Zou Qiurong Yan Qianling Dai Ao Wang Bo Yang Yi Li Jinwei Yan |
| author_facet | Quan Zou Qiurong Yan Qianling Dai Ao Wang Bo Yang Yi Li Jinwei Yan |
| author_sort | Quan Zou |
| collection | DOAJ |
| description | Single-pixel imaging (SPI), an imaging technique based on the theory of compressed sensing, is limited in real-time imaging and high-resolution images due to its relatively slow imaging speed. In recent years, deep unfolding network compressed sensing reconstruction algorithms based on deep learning have proven to be an effective solution for faster and higher quality image reconstruction. However, existing deep unfolding networks mainly rely on a single piece of a priori information and may ignore other intrinsic structures of the image. Therefore, in this paper, we propose a deep unfolding network (MPDU-Net) that incorporates multiple prior information. To effectively fuse multiple prior information, we propose three different fusion strategies in the deep reconstruction sub-network. An unbiased convolutional layer is used to simulate the sampling reconstruction process to achieve joint reconstruction for effective removal of block artifacts. The sampling matrix is input into the deep reconstruction sub-network as a learnable parameter to achieve joint optimization of sampling reconstruction. Simulation and practical experimental results show that the proposed network outperforms existing compressed sensing reconstruction algorithms based on deep unfolding networks. |
| format | Article |
| id | doaj-art-a01365a4697c469ab2b700f77db16fe9 |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-a01365a4697c469ab2b700f77db16fe92025-08-20T03:33:21ZengIEEEIEEE Photonics Journal1943-06552024-01-0116411010.1109/JPHOT.2024.342078710577085Single Pixel Imaging Based on Multiple Prior Deep Unfolding NetworkQuan Zou0https://orcid.org/0009-0003-7280-3953Qiurong Yan1https://orcid.org/0000-0003-4736-7435Qianling Dai2https://orcid.org/0009-0001-1735-2606Ao Wang3Bo Yang4https://orcid.org/0009-0002-0752-482XYi Li5Jinwei Yan6School of Information Engineering, Nanchang University, Nanchang, ChinaSchool of Information Engineering, Nanchang University, Nanchang, ChinaSchool of Information Engineering, Nanchang University, Nanchang, ChinaSchool of Information Engineering, Nanchang University, Nanchang, ChinaSchool of Information Engineering, Nanchang University, Nanchang, ChinaSchool of Information Engineering, Nanchang University, Nanchang, ChinaSchool of Information Engineering, Nanchang University, Nanchang, ChinaSingle-pixel imaging (SPI), an imaging technique based on the theory of compressed sensing, is limited in real-time imaging and high-resolution images due to its relatively slow imaging speed. In recent years, deep unfolding network compressed sensing reconstruction algorithms based on deep learning have proven to be an effective solution for faster and higher quality image reconstruction. However, existing deep unfolding networks mainly rely on a single piece of a priori information and may ignore other intrinsic structures of the image. Therefore, in this paper, we propose a deep unfolding network (MPDU-Net) that incorporates multiple prior information. To effectively fuse multiple prior information, we propose three different fusion strategies in the deep reconstruction sub-network. An unbiased convolutional layer is used to simulate the sampling reconstruction process to achieve joint reconstruction for effective removal of block artifacts. The sampling matrix is input into the deep reconstruction sub-network as a learnable parameter to achieve joint optimization of sampling reconstruction. Simulation and practical experimental results show that the proposed network outperforms existing compressed sensing reconstruction algorithms based on deep unfolding networks.https://ieeexplore.ieee.org/document/10577085/Single pixel imagingdeep unfolding networkmultiple prior informationjoint optimization |
| spellingShingle | Quan Zou Qiurong Yan Qianling Dai Ao Wang Bo Yang Yi Li Jinwei Yan Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network IEEE Photonics Journal Single pixel imaging deep unfolding network multiple prior information joint optimization |
| title | Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network |
| title_full | Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network |
| title_fullStr | Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network |
| title_full_unstemmed | Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network |
| title_short | Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network |
| title_sort | single pixel imaging based on multiple prior deep unfolding network |
| topic | Single pixel imaging deep unfolding network multiple prior information joint optimization |
| url | https://ieeexplore.ieee.org/document/10577085/ |
| work_keys_str_mv | AT quanzou singlepixelimagingbasedonmultiplepriordeepunfoldingnetwork AT qiurongyan singlepixelimagingbasedonmultiplepriordeepunfoldingnetwork AT qianlingdai singlepixelimagingbasedonmultiplepriordeepunfoldingnetwork AT aowang singlepixelimagingbasedonmultiplepriordeepunfoldingnetwork AT boyang singlepixelimagingbasedonmultiplepriordeepunfoldingnetwork AT yili singlepixelimagingbasedonmultiplepriordeepunfoldingnetwork AT jinweiyan singlepixelimagingbasedonmultiplepriordeepunfoldingnetwork |