SAR Image Denoising Based on Similarity Validation and Patch Ordering in NSST Domain

被引：0
|
作者：
Liu S.-Q. [1 ,2 ,3 ]
Hu Q. [1 ,2 ,3 ]
Li Z. [4 ]
An Y.-L. [1 ,2 ,3 ]
Li P.-F. [1 ,2 ,3 ]
Zhao J. [1 ,2 ,3 ]
机构：
[1] College of Electronic and Information Engineering, Hebei University, Baoding, 071000, Hebei
[2] Machine Vision Engineering Research Center of Hebei Province, Baoding, 071000, Hebei
[3] Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071000, Hebei
[4] Department of UAV Engineering, Army Engineering University, Shijiazhuang, 050003, Hebei
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2018年 / 38卷 / 07期
关键词：
Non-subsampling shearlet transform; Patch ordering; SAR image denoising; Similarity validation;
D O I：
10.15918/j.tbit1001-0645.2018.07.014
中图分类号：
学科分类号：
摘要：
In order to overcome the shortcoming of traditional synthetic aperture radar(SAR)image denoising algorithm in non-local transform domain without considering the patch relationship, a new SAR image denoising algorithm in non-subsampled shearlet transform(NSST)domain was proposed based on similarity validation and patch ordering.Firstly, the density distribution of distances between similar patches of SAR image in the NSST domain was constructed.Then, the patches with lower similarity were removed according to the similarity between the patches.Finally, SAR image was denoised by combining the patch ordering and the optimal one-dimensional filter.The experimental results show that, compared with other transform domain algorithms, the equivalent numbers of looks in this algorithm can increase by 6.92 on average, and the edge preservation index is close to 1.And the unassisted measure of quality can reduce by 2.51 on average.The algorithm can better maintain the image edge and texture information, and improve the visual effect of the image. © 2018, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.
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页码：744 / 751
页数：7
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