Unsupervised Deep Hyperspectral Image Super-Resolution
This chapter presents the recent advanced deep unsupervised hyperspectral (HS) image super-resolution framework for automatically generating a high-resolution (HR) HS image from its low-resolution (LR) HS and high-resolution RGB observations without any external sample. We incorporate the deep learn...
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| Autores principales: | , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2022
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/83539 |
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| Sumario: | This chapter presents the recent advanced deep unsupervised hyperspectral (HS) image super-resolution framework for automatically generating a high-resolution (HR) HS image from its low-resolution (LR) HS and high-resolution RGB observations without any external sample. We incorporate the deep learned priors of the underlying structure in the latent HR-HS image with the mathematical model for formulating the degradation procedures of the observed LR-HS and HR-RGB observations and introduce an unsupervised end-to-end deep prior learning network for robust HR-HS image recovery. Experiments on two benchmark datasets validated that the proposed method manifest very impressive performance, and is even better than most state-of-the-art supervised learning approaches. |
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| ISBN: | 978-1-83969-093-8 |
| DOI: | 10.5772/intechopen.106908 |