Hyperspectral Image Denoising via Correntropy-Based Nonconvex Low-Rank Approximation
Hyperspectral images (HSIs) are prone to be corrupted by various types of noise during the process of imaging and transmission, which seriously affect the subsequent HSI processing tasks. In this article, we proposed a novel low-rank-based model for HSIs denoising. On one hand, motivated by the supe...
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| Asıl Yazarlar: | , , , |
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| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
IEEE
2024-01-01
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| Seri Bilgileri: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Konular: | |
| Online Erişim: | https://ieeexplore.ieee.org/document/10460099/ |
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