Hyperspectral Image Denoising with Composite Regularization Models
Joint Authors
Deyun, Chen
Li, Ao
Lin, Kezheng
Sun, Guanglu
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-05-16
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Denoising is a fundamental task in hyperspectral image (HSI) processing that can improve the performance of classification, unmixing, and other subsequent applications.
In an HSI, there is a large amount of local and global redundancy in its spatial domain that can be used to preserve the details and texture.
In addition, the correlation of the spectral domain is another valuable property that can be utilized to obtain good results.
Therefore, in this paper, we proposed a novel HSI denoising scheme that exploits composite spatial-spectral information using a nonlocal technique (NLT).
First, a specific way to extract patches is employed to mine the spatial-spectral knowledge effectively.
Next, a framework with composite regularization models is used to implement the denoising.
A number of HSI data sets are used in our evaluation experiments and the results demonstrate that the proposed algorithm outperforms other state-of-the-art HSI denoising methods.
American Psychological Association (APA)
Li, Ao& Deyun, Chen& Lin, Kezheng& Sun, Guanglu. 2016. Hyperspectral Image Denoising with Composite Regularization Models. Journal of Sensors،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1110561
Modern Language Association (MLA)
Li, Ao…[et al.]. Hyperspectral Image Denoising with Composite Regularization Models. Journal of Sensors No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1110561
American Medical Association (AMA)
Li, Ao& Deyun, Chen& Lin, Kezheng& Sun, Guanglu. Hyperspectral Image Denoising with Composite Regularization Models. Journal of Sensors. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1110561
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110561