Journal
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
Volume -, Issue -, Pages 1492-1495Publisher
IEEE
DOI: 10.1109/IGARSS39084.2020.9323489
Keywords
hyperspectral image; compressed sensing; mixed noise removal; total variation; primal-dual splitting method
Categories
Funding
- JST CREST [JP-MJCR1662, JPMJCR1666]
- JSPS KAKENHI [18J20290, 18H05413]
- Grants-in-Aid for Scientific Research [18J20290] Funding Source: KAKEN
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Compressed sensing (CS) reconstruction is essential in capturing hyperspectral (HS) images by one-shot. However, existing approaches for compressed HS imaging assume that compressed observation is contaminated by Gaussian noise, and so they are sensitive to the other type of noise and outliers. To resolve the above problem, we propose a new methodology compressed HS imaging that can handle mixed Gaussian-sparse noise. For robust estimation, our proposed method simultaneously estimates a clean HS image and sparse noise by solving a convex optimization problem. Experimental results illustrate the utility of our proposed framework.
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