| Autor: |
Prévost, Clémence, Chainais, Pierre, Boyer, Remy |
| Přispěvatelé: |
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
2022 IEEE International Conference on Image Processing (ICIP). |
| DOI: |
10.1109/icip46576.2022.9898065 |
| Popis: |
Hyperspectral super-resolution based on coupled Tucker decomposition has been recently considered in the remote sensing community. The state-of-the-art approaches did not fully exploit the coupling information contained in hyperspectral and multispectral images of the same scene. In this paper, we propose a new algorithm that overcomes this limitation. It accounts for both the high-resolution and the low-resolution information in the model, by solving a set of leastsquares problems. In addition, we provide exact recovery conditions for the super-resolution image in the noiseless case. Our simulations show that the proposed algorithm achieves good reconstruction with low complexity. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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