Hyperspectral complex domain denoising

被引：0
|
作者：
Katkovnik, Vladimir [1 ]
Shevkunov, Igor [1 ]
Egiazarian, Karen [1 ]
机构：
[1] Tampere Univ, Fac Informat Technol & Commun Sci, Computat Imaging Grp, Tampere, Finland
来源：
2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | 2019年
基金：
芬兰科学院;
关键词：
Hyperspectral imaging; singular value decomposition; sparse representation; noise filtering; noise in imaging systems; PHASE;
D O I：
10.23919/eusipco.2019.8903100
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We consider hyperspectral complex domain imaging from hyperspectral complex-valued noisy observations. The proposed algorithm is based on singular value decomposition (SVD) of observations and complex domain block-matching 3D (CDBM3D) filtering in optimized SVD eigenspace. Simulation experiments demonstrate high efficiency of the proposed complex domain joint filtering of hyperspectral data in comparison with CDBM3D filtering of separate 2D slices of hyperspectral cubes as well as with respect to joint real domain independent phase/amplitude filtering this kind of data.
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页数：5
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