A method of reconstructing compressive spectral imaging with a complementary prior constraint
Compressed spectral imaging (CSI) is a technique for acquiring a cube of spectral image data in a single snapshot. In this paper, we propose a reconstruction method for the CSI system that integrates complementary prior constraints on spectral data to significantly enhance the accuracy of reconstruc...
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| Vydáno v: | Optics communications Ročník 550; s. 130010 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.01.2024
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| ISSN: | 0030-4018, 1873-0310 |
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| Abstract | Compressed spectral imaging (CSI) is a technique for acquiring a cube of spectral image data in a single snapshot. In this paper, we propose a reconstruction method for the CSI system that integrates complementary prior constraints on spectral data to significantly enhance the accuracy of reconstructed data. The fundamental concepts are as follows: (1) Firstly, by conducting a thorough analysis of the corresponding feature maps of spectral data on the convolution dictionaries, we confirm the feasibility of utilizing the stationary response characteristics of spectral data on the convolution dictionaries as a reconstruction constraint to enhance spectral accuracy. (2) To compensate for the limitations of Convolutional Sparse Coding in reconstructing low-frequency signals, this paper proposes utilizing iterative Total Variation operators to estimate the low-frequency portion of spectral data. This approach results in improved performance in reconstructing low-frequency data and noise suppression. (3) Convolutional Sparse Coding and Total Variation are utilized as constraints for high and low-frequency complementary reconstruction, resulting in a multi-constraint solution problem within the Plug-and-Play framework that simplifies the overall reconstruction process. The proposed method surpasses the state-of-the-art reconstruction methods in both spatial reconstruction quality and spectral accuracy, while also significantly enhancing the level of detail in reconstructed spectral images.
•Convolutional sparse coding shows stationary characteristics on spectral data.•Additional low frequency estimation is an important part of convolutional sparse coding.•Frequency complementary constraints can improve the accuracy of compressed sensing spectral imaging.•Iterative low frequency estimation can better estimate low frequency signal. |
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| AbstractList | Compressed spectral imaging (CSI) is a technique for acquiring a cube of spectral image data in a single snapshot. In this paper, we propose a reconstruction method for the CSI system that integrates complementary prior constraints on spectral data to significantly enhance the accuracy of reconstructed data. The fundamental concepts are as follows: (1) Firstly, by conducting a thorough analysis of the corresponding feature maps of spectral data on the convolution dictionaries, we confirm the feasibility of utilizing the stationary response characteristics of spectral data on the convolution dictionaries as a reconstruction constraint to enhance spectral accuracy. (2) To compensate for the limitations of Convolutional Sparse Coding in reconstructing low-frequency signals, this paper proposes utilizing iterative Total Variation operators to estimate the low-frequency portion of spectral data. This approach results in improved performance in reconstructing low-frequency data and noise suppression. (3) Convolutional Sparse Coding and Total Variation are utilized as constraints for high and low-frequency complementary reconstruction, resulting in a multi-constraint solution problem within the Plug-and-Play framework that simplifies the overall reconstruction process. The proposed method surpasses the state-of-the-art reconstruction methods in both spatial reconstruction quality and spectral accuracy, while also significantly enhancing the level of detail in reconstructed spectral images.
•Convolutional sparse coding shows stationary characteristics on spectral data.•Additional low frequency estimation is an important part of convolutional sparse coding.•Frequency complementary constraints can improve the accuracy of compressed sensing spectral imaging.•Iterative low frequency estimation can better estimate low frequency signal. |
| ArticleNumber | 130010 |
| Author | Wang, Feng ping Wang, Lin Wang, Pan Li, Jie Qi, Chun |
| Author_xml | – sequence: 1 givenname: Pan orcidid: 0000-0002-1905-5269 surname: Wang fullname: Wang, Pan – sequence: 2 givenname: Jie surname: Li fullname: Li, Jie email: jielixjtu@xjtu.edu.cn – sequence: 3 givenname: Chun surname: Qi fullname: Qi, Chun – sequence: 4 givenname: Lin surname: Wang fullname: Wang, Lin – sequence: 5 givenname: Feng ping surname: Wang fullname: Wang, Feng ping |
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| Cites_doi | 10.1109/MSP.2007.914730 10.1109/TPAMI.2021.3099035 10.1109/TPAMI.2018.2873587 10.1109/MSP.2020.3023869 10.1109/JSTARS.2019.2902332 10.1364/AO.45.002965 10.1063/1.5140721 10.1109/MSP.2022.3199595 10.1109/MSP.2007.914731 10.1109/TIP.2007.909319 10.1364/AO.420305 10.1109/TIP.2014.2344294 10.1561/2200000016 10.1109/TIP.2015.2495260 10.1145/2661229.2661262 10.1109/TIP.2021.3086049 10.1109/JSTSP.2007.910281 10.1016/j.acha.2015.03.003 10.1109/TIP.2003.819861 10.1109/TIP.2014.2365720 10.1364/OE.15.014013 10.1109/JSTSP.2015.2411575 10.1364/AO.47.000B44 10.1111/cgf.13086 10.1023/B:JMIV.0000011325.36760.1e 10.1364/OPTICA.6.000921 |
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| Keywords | Plug-and-Play Compressive spectral imaging Convolutional sparse coding |
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| Snippet | Compressed spectral imaging (CSI) is a technique for acquiring a cube of spectral image data in a single snapshot. In this paper, we propose a reconstruction... |
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| Title | A method of reconstructing compressive spectral imaging with a complementary prior constraint |
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