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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2306.10489 |
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| _version_ | 1866913291295522816 |
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| author | Cui, Xiaofei Chang, Jingya |
| author_facet | Cui, Xiaofei Chang, Jingya |
| contents | Hyperspectral image (HSI) and multispectral image (MSI) fusion aims at producing a super-resolution image (SRI). In this paper, we establish a nonconvex optimization model for image fusion problems through low-rank tensor triple decomposition. Using the L-BFGS approach, we develop a first-order optimization algorithm for obtaining the desired super-resolution image (TTDSR). Furthermore, two detailed methods are provided for calculating the gradient of the objective function. With the aid of the Kurdyka-Lojasiewicz property, the iterative sequence is proved to converge to a stationary point. Finally, experimental results on different datasets show the effectiveness of our proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_10489 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Hyperspectral super-resolution via low rank tensor triple decomposition Cui, Xiaofei Chang, Jingya Optimization and Control Hyperspectral image (HSI) and multispectral image (MSI) fusion aims at producing a super-resolution image (SRI). In this paper, we establish a nonconvex optimization model for image fusion problems through low-rank tensor triple decomposition. Using the L-BFGS approach, we develop a first-order optimization algorithm for obtaining the desired super-resolution image (TTDSR). Furthermore, two detailed methods are provided for calculating the gradient of the objective function. With the aid of the Kurdyka-Lojasiewicz property, the iterative sequence is proved to converge to a stationary point. Finally, experimental results on different datasets show the effectiveness of our proposed approach. |
| title | Hyperspectral super-resolution via low rank tensor triple decomposition |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2306.10489 |