Nonlocal Patch Tensor Sparse Representation for Hyperspectral Image Super-Resolution
This paper presents a hypserspectral image (HSI) super-resolution method, which fuses a low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor...
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| Published in: | IEEE transactions on image processing Vol. 28; no. 6; pp. 3034 - 3047 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
IEEE
01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers |
| Subjects: | |
| ISSN: | 1057-7149, 1941-0042, 1941-0042 |
| Online Access: | Get full text |
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| Abstract | This paper presents a hypserspectral image (HSI) super-resolution method, which fuses a low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor-tensor product (t - product)-based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and the LR-HSI is built using t - product, which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, alternating direction method of multipliers is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-ofthe-art HSI super-resolution methods. |
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| AbstractList | This paper presents a hypserspectral image (HSI) super-resolution method which fuses a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor-tensor product (t-product) based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and LR-HSI is built using t-product which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, Alternating Direction Method of Multipliers (ADMM) is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-of-the-art HSI super-resolution methods.This paper presents a hypserspectral image (HSI) super-resolution method which fuses a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor-tensor product (t-product) based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and LR-HSI is built using t-product which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, Alternating Direction Method of Multipliers (ADMM) is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-of-the-art HSI super-resolution methods. This paper presents a hypserspectral image (HSI) super-resolution method, which fuses a low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor–tensor product ([Formula Omitted])-based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and the LR-HSI is built using [Formula Omitted], which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, alternating direction method of multipliers is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-of-the-art HSI super-resolution methods. This paper presents a hypserspectral image (HSI) super-resolution method, which fuses a low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor-tensor product (t - product)-based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and the LR-HSI is built using t - product, which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, alternating direction method of multipliers is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-ofthe-art HSI super-resolution methods. This paper presents a hypserspectral image (HSI) super-resolution method which fuses a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor-tensor product (t-product) based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and LR-HSI is built using t-product which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, Alternating Direction Method of Multipliers (ADMM) is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-of-the-art HSI super-resolution methods. |
| Author | Chanussot, Jocelyn Yang Xu Zhihui Wei Zebin Wu |
| Author_xml | – sequence: 1 surname: Yang Xu fullname: Yang Xu email: xuyangth90@njust.edu.cn organization: Sch. of Comput. Sci. & Eng., Nanjing Univ. of Sci. & Technol., Nanjing, China – sequence: 2 surname: Zebin Wu fullname: Zebin Wu email: zebin.wu@gmail.com organization: Sch. of Comput. Sci. & Eng., Nanjing Univ. of Sci. & Technol., Nanjing, China – sequence: 3 givenname: Jocelyn surname: Chanussot fullname: Chanussot, Jocelyn email: jocelyn@hi.is organization: GIPSA-Lab., Univ. Grenoble Alpes, Grenoble, France – sequence: 4 surname: Zhihui Wei fullname: Zhihui Wei email: gswei@njust.edu.cn organization: Sch. of Comput. Sci. & Eng., Nanjing Univ. of Sci. & Technol., Nanjing, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30668472$$D View this record in MEDLINE/PubMed https://hal.science/hal-02307490$$DView record in HAL |
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| Snippet | This paper presents a hypserspectral image (HSI) super-resolution method, which fuses a low-resolution HSI (LR-HSI) with a high-resolution multispectral image... This paper presents a hypserspectral image (HSI) super-resolution method which fuses a low-resolution hyperspectral image (LR-HSI) with a high-resolution... |
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| SubjectTerms | Dictionaries Engineering Sciences Fuses High resolution Hyperspectral image Hyperspectral imaging Image resolution Mathematical analysis nonlocal patch tensor Optimization Representations Signal and Image processing Signal resolution Sparse matrices Spatial resolution super-resolution tensor dictionary learning tensor sparse coding Tensors |
| Title | Nonlocal Patch Tensor Sparse Representation for Hyperspectral Image Super-Resolution |
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