SAR Image De-noising Based on Generalized Non-local Means in Non-subsample Shearlet Domain

被引：4
|
作者：
Liu Shuaiqi [1 ,2 ]
Geng Peng [3 ]
Shi Mingzhu [4 ]
Fang Jing [5 ]
Hu Shaohai [5 ]
机构：
[1] Hebei Univ, Coll Elect & Informat Engn, Baoding, Hebei, Peoples R China
[2] Key Lab Digital Med Engn Hebei Prov, Baoding, Hebei, Peoples R China
[3] Shijiazhuang Tiedao Univ, Sch Informat Sci & Technol, Shijiazhuang, Hebei, Peoples R China
[4] Tianjin Normal Univ, Coll Elect & Commun Engn, Tianjin, Peoples R China
[5] Beijing Jiaotong Univ, Inst Informat Sci, Beijing, Peoples R China
来源：
PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON COMMUNICATIONS, SIGNAL PROCESSING, AND SYSTEMS | 2016年 / 386卷
关键词：
SPARSE REPRESENTATION; TRANSFORM; SHRINKAGE;
D O I：
10.1007/978-3-662-49831-6_23
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：
How to suppress and remove the speckle of SAR image has been a hot research issue. Combining the advantages of non-subsample Shearlet transform (NSST) with the generalized non-local means de-noising algorithm, we proposed a new SAR image de-noising algorithm in this paper. This algorithm is appropriate for the characteristics of the speckle noise, so it can improve the quality of de-noised image. Meanwhile, the algorithm holds the characteristics of translational invariance, which can suppress Gibbs phenomenon effectively.
引用
收藏
页码：221 / 229
页数：9
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