Synthetic aperture radar image despeckling via total generalised variation approach
Feng, Wensen1; Lei, Hong2; Qiao, Hong1,3
Source PublicationIET IMAGE PROCESSING
2015-03-01
Volume9Issue:3Pages:236-248
SubtypeArticle
AbstractSpeckle reduction is an important task in synthetic aperture radar. One extensively used approach is based on total variation (TV) regularisation, which can realise significantly sharp edges, but on the other hand brings in the undesirable staircasing artefacts. In essence, the TV-based methods tend to create piecewise-constant images even in regions with smooth transitions. In this study, a new method is proposed for speckle reduction via total generalised variation (TGV) penalty. This is reasonable from the fact that the TGV-based model can reduce the staircasing artefacts of TV by being aware of higher-order smoothness. An efficient numerical scheme based on the Nesterov's algorithm is also developed for solving the TGV-based optimisation problem. Monte Carlo experiments show that the proposed scheme yields state-of-the-art results in terms of both performance and speed. Especially when the image has some higher-order smoothness, the authors' scheme outperforms the TV-based methods.
KeywordSynthetic Aperture Radar Radar Imaging Image Denoising Speckle Variational Techniques Piecewise Constant Techniques Smoothing Methods Higher Order Statistics Optimisation Monte Carlo Methods Synthetic Aperture Radar Image Despeckling Total Generalised Variation Approach Speckle Reduction Total Variation Regularisation Piecewise Constant Image Smooth Transition Tgv-based Model Staircasing Artefact Reduction Higher Order Smoothness Numerical Scheme Nesterov Algorithm Tgv-based Optimisation Problem Monte Carlo Method
WOS HeadingsScience & Technology ; Technology
WOS KeywordMULTIPLICATIVE NOISE REMOVAL ; SAR IMAGES ; SPECKLE ; FILTERS ; MODEL ; MINIMIZATION ; RECOVERY ; DOMAIN
Indexed BySCI
Language英语
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000350799900008
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8087
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Affiliation1.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Elect, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Feng, Wensen,Lei, Hong,Qiao, Hong. Synthetic aperture radar image despeckling via total generalised variation approach[J]. IET IMAGE PROCESSING,2015,9(3):236-248.
APA Feng, Wensen,Lei, Hong,&Qiao, Hong.(2015).Synthetic aperture radar image despeckling via total generalised variation approach.IET IMAGE PROCESSING,9(3),236-248.
MLA Feng, Wensen,et al."Synthetic aperture radar image despeckling via total generalised variation approach".IET IMAGE PROCESSING 9.3(2015):236-248.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Feng, Wensen]'s Articles
[Lei, Hong]'s Articles
[Qiao, Hong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Feng, Wensen]'s Articles
[Lei, Hong]'s Articles
[Qiao, Hong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Feng, Wensen]'s Articles
[Lei, Hong]'s Articles
[Qiao, Hong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.