朱磊^1,,,
李敬曼¹,
潘杨¹,
刘玉春^{1, 2},
胡晓¹
1.西安工程大学电子信息学院 西安 710048
2.周口师范学院机械与电气工程学院 周口 466001
基金项目:国家自然科学基金(61971339)，陕西省重点研发计划(2019GY-113)，西安市科技局创新引导计划(201805030YD8CG14(6))

详细信息

作者简介:朱磊：男，1979年生，教授，硕士生导师，研究方向为图像处理、嵌入式系统应用
李敬曼：女，1996年生，硕士生，研究方向为图像处理
潘杨：女，1983年生，讲师，研究方向为数字信号处理、声场仿真与声信号处理
刘玉春：男，1979年生，副教授，研究方向为信号与信号处理
胡晓：女，1993年生，硕士生，研究方向为图像处理

通讯作者:朱磊　zhulei791014@163.com

中图分类号:TN911.73; TP751

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被引次数:0

出版历程

收稿日期:2020-02-11
修回日期:2020-09-09
网络出版日期:2020-09-15
刊出日期:2021-05-18

SAR Image Despeckling Algorithm Using Non-Local Means with Adaptive Filtering Strength

Lei ZHU^1,,,
Jingman LI¹,
Yang PAN¹,
Yuchun LIU^{1, 2},
Xiao HU¹
1. School of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, China
2. School of Mechanical and Electrical Engineering, Zhoukou Normal University, Zhoukou 466001, China
Funds:The National Natural Science Foundation of China (61971339), The Shaanxi Provincial Key Research and Development Program (2019GY-113), The Xi’an Science and Technology Bureau Innovation and Guidance Program (201805030YD8CG14(6))


摘要
摘要:为提升对SAR图像乘性相干斑的抑制水平与边缘保护性能，该文提出了一种可自适应调节滤波强度(AFS)的SAR图像非局部平均(NLM)抑斑新算法(AFS-NLM)。该算法利用Frost滤波图像计算的局部均值与方差来改善SAR图像场景参量的估计，形成了一种能更好刻画SAR图像同质区与边缘区的改进Kuan滤波系数。利用局部均值比与改进Kuan滤波系数分别作为新的相似性测量参量与自适应衰减因子，构建了一种更适应SAR图像乘性噪声特性的改进NLM滤波。利用偏平滑参数与偏边缘保护参数控制下的改进NLM滤波，分别替代经典Kuan滤波模型中的像素局部均值与自身灰度值作为加权项，并采用由改进Kuan滤波系数构建的自适应调节因子对二者进行加权平均，从而形成了一种可自适应调节滤波强度的加权滤波新模型。实验表明，该文算法与近期多种先进算法相比，具有更好的相干斑抑制与边缘保护性能。
关键词:SAR图像/
相干斑抑制/
自适应滤波强度/
非局部平均/
改进Kuan滤波
Abstract:A new Non-Local Means (NLM) despeckling algorithm (AFS-NLM) with Adaptive Filtering Strength (AFS) is proposed to improve the performance of reducing multiplicative speckle and preserving the edges in SAR images. A modified Kuan filtering coefficient which can better characterize the homogeneous and edge regions of SAR image is formed by using the local mean and variance calculated in the Frost filtered image to improve the estimation of SAR image scene parameters. An improved NLM which adapts to the multiplicative noise characteristics is constructed by the new similarity measurement parameter estimated by the local mean ratio and the new adaptive decay factor estimated by the improved Kuan filtering coefficient. A new weighted filtering model which can automatically adjust the filtering strength is formed. In the new model, the improved NLM filters controlled by the skew smoothing parameters and the skew edge protection parameters are used to replace the local average value of pixels and the gray value of pixels in the classic Kuan filter model as weighting items, and the adaptive adjustment factor constructed by the improved Kuan filter coefficient is used to weight the two items. Experimental results and comparisons with several advanced despeckling algorithms in recent years show that the proposed algorithm has better speckle suppression and edge preservation performance.
Key words:SAR image/
Speckle suppression/
Adaptive Filtering Strength (AFS)/
Non-Local Means (NLM)/
Improved Kuan filtering



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