自适应调节滤波强度的SAR图像非局部平均抑斑算法

朱磊; 李敬曼; 潘杨; 刘玉春; 胡晓

doi:10.11999/JEIT200099

自适应调节滤波强度的SAR图像非局部平均抑斑算法

doi: 10.11999/JEIT200099

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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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。
- 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.
- SAR image /
- Speckle suppression /
- Adaptive Filtering Strength (AFS) /
- Non-Local Means (NLM) /
- Improved Kuan filtering

HTML全文

图 1 自适应调节NLM滤波强度的SAR图像抑斑新模型框图

下载: 全尺寸图片幻灯片

图 2 Kuan滤波系数与改进Kuan滤波系数对比

下载: 全尺寸图片幻灯片

图 3 两种方法估计的NLM滤波加权系数图对比

下载: 全尺寸图片幻灯片

图 4 实验测试用真实SAR图像

下载: 全尺寸图片幻灯片

图 5 各算法对图4两幅真实SAR图像的抑斑图及其边缘检测图对比

下载: 全尺寸图片幻灯片

表 1 4种算法对真实SAR图像抑斑参数比较

抑斑算法	${V_{\rm{ENL}}}$				${V_{\rm{EPI}}}$
抑斑算法	A区	B区	C区	D区	图4(a)	图4(b)
SAR-BM3D	755.8	332.0	1610.8	1703.1	0.944	0.771
NL-CV	2070.0	788.9	1001.1	3171.9	0.449	0.400
MR-NLM	2485.4	826.1	1774.1	4100.1	0.958	0.780
AFS-NLM	5064.6	2312.8	3555.3	24115	0.963	0.824

下载: 导出CSV

参考文献(23)

[1]	魏松杰, 蒋鹏飞, 袁秋壮, 等. 深度神经网络下的SAR舰船目标检测与区分模型[J]. 西北工业大学学报, 2019, 37(3): 587–593. doi: 10.1051/jnwpu/20193730587 WEI Songjie, JIANG Pengfei, YUAN Qiuzhuang, et al. Detection and recognition of SAR small ship objects using deep neural network[J]. Journal of Northwestern Polytechnical University, 2019, 37(3): 587–593. doi: 10.1051/jnwpu/20193730587
[2]	LIU Su, ZHANG Gong, and LIU Wenbo. Group sparse representation based dictionary learning for SAR image despeckling[J]. IEEE Access, 2019, 7: 30809–30817. doi: 10.1109/ACCESS.2019.2859825
[3]	李煜, 陈杰, 张渊智. 合成孔径雷达海面溢油探测研究进展[J]. 电子与信息学报, 2019, 41(3): 751–762. doi: 10.11999/JEIT180468 LI Yu, CHEN Jie, and ZHANG Yuanzhi. Progress in research on marine oil spills detection using synthetic aperture radar[J]. Journal of Electronics &Information Technology, 2019, 41(3): 751–762. doi: 10.11999/JEIT180468
[4]	吴元. 一种基于参数更新的机载SAR图像目标定位方法[J]. 电子与信息学报, 2019, 41(5): 1063–1068. doi: 10.11999/JEIT180564 WU Yuan. An airborne SAR image target location algorithm based on parameter refining[J]. Journal of Electronics &Information Technology, 2019, 41(5): 1063–1068. doi: 10.11999/JEIT180564
[5]	彭书娟, 曲长文, 李建伟, 等. 基于ROEWA算子局部活动轮廓的SAR图像分割算法[J]. 系统工程与电子技术, 2019, 41(2): 280–290. doi: 10.3969/j.issn.1001-506X.2019.02.09 PENG Shujuan, QU Changwen, LI Jianwei, et al. Local motion contour segmentation algorithm of SAR image based on ROEWA operator[J]. Systems Engineering and Electronics, 2019, 41(2): 280–290. doi: 10.3969/j.issn.1001-506X.2019.02.09
[6]	韩子硕, 王春平. 基于改进FCM与MRF的SAR图像分割[J]. 系统工程与电子技术, 2019, 41(8): 1726–1734. doi: 10.3969/j.issn.1001-506X.2019.08.08 HAN Zishuo and WANG Chunping. SAR image segmentation based on improved FCM and MRF[J]. Systems Engineering and Electronics, 2019, 41(8): 1726–1734. doi: 10.3969/j.issn.1001-506X.2019.08.08
[7]	YU Meiting, QUAN Sinong, KUANG Gangyao, et al. SAR target recognition via joint sparse and dense representation of monogenic signal[J]. Remote Sensing, 2019, 11(22): 2676. doi: 10.3390/rs11222676
[8]	LEE J S. Digital image enhancement and noise filtering by use of local statistics[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980, PAMI-2(2): 165–168. doi: 10.1109/TPAMI.1980.4766994
[9]	KUAN D T, SAWCHUK A A, STRAND T C, et al. Adaptive noise smoothing filter for images with signal-dependent noise[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1985, PAMI-7(2): 165–177. doi: 10.1109/TPAMI.1985.4767641
[10]	MA Xiaoshuang and WU Penghai. Multitemporal SAR image despeckling based on a scattering covariance matrix of image patch[J]. Sensors, 2019, 19(14): 3057. doi: 10.3390/s19143057
[11]	BHUIYAN M I H, AHMAD M, and SWAMY M N S. Spatially adaptive wavelet-based method using the cauchy prior for denoising the SAR images[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2007, 17(4): 500–507. doi: 10.1109/TCSVT.2006.888020
[12]	CHOI H and JEONG J. Speckle noise reduction technique for SAR images using statistical characteristics of speckle noise and discrete wavelet transform[J]. Remote Sensing, 2019, 11(10): 1184. doi: 10.3390/rs11101184
[13]	GAO Fei, XUE Xiangshang, SUN Jinping, et al. A SAR image despeckling method based on two-dimensional S transform shrinkage[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(5): 3025–3034. doi: 10.1109/TGRS.2015.2510161
[14]	YU Yongjian and ACTON S T. Speckle reducing anisotropic diffusion[J]. IEEE Transactions on Image Processing, 2002, 11(11): 1260–1270. doi: 10.1109/TIP.2002.804276
[15]	ZHU Lei, ZHAO Xiaotian, and GU Meihua. SAR image despeckling using improved detail-preserving anisotropic diffusion[J]. Electronics Letters, 2014, 50(15): 1092–1093. doi: 10.1049/el.2014.0293
[16]	MISHRA D, CHAUDHURY S, SARKAR M, et al. Edge probability and pixel relativity-based speckle reducing anisotropic diffusion[J]. IEEE Transactions on Image Processing, 2018, 27(2): 649–664. doi: 10.1109/TIP.2017.2762590
[17]	BUADES A, COLL B, and MOREL J M. A non-local algorithm for image denoising[C]. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, USA, 2005: 60–65. doi: 10.1109/CVPR.2005.38.
[18]	PARRILLI S, PODERICO M, ANGELINO C V, et al. A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(2): 606–616. doi: 10.1109/TGRS.2011.2161586
[19]	CHEN Shaobo, HOU Jianhua, ZHANG Hua, et al. De-speckling method based on non-local means and coefficient variation of SAR image[J]. Electronics Letters, 2014, 50(18): 1314–1316. doi: 10.1049/el.2014.0630
[20]	朱磊, 蔡飞飞, 王延年, 等. SAR图像相干斑的非局部平均滤波算法[J]. 西安交通大学学报, 2018, 52(4): 98–104. doi: 10.7652/xjtuxb201804014 ZHU Lei, CAI Feifei, WANG Yannian, et al. A non-local means filtering algorithm for despeckling of SAR images[J]. Journal of Xi’an Jiaotong University, 2018, 52(4): 98–104. doi: 10.7652/xjtuxb201804014
[21]	FROST V S, STILES J A, SHANMUGAN K S, et al. A model for radar images and its application to adaptive digital filtering of multiplicative noise[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982, PAMI-4(2): 157–165. doi: 10.1109/TPAMI.1982.4767223
[22]	TOUZI R, LOPES A, and BOUSQUET P. A statistical and geometrical edge detector for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1988, 26(6): 764–773. doi: 10.1109/36.7708
[23]	朱磊, 水鹏朗, 章为川, 等. 利用区域划分的合成孔径雷达图像相干斑抑制算法[J]. 西安交通大学学报, 2012, 46(10): 83–88, 100. ZHU Lei, SHUI Penglang, ZHANG Weichuan, et al. A despeckling algorithm for synthetic aperture radar images using region subdivision[J]. Journal of Xi’an Jiaotong University, 2012, 46(10): 83–88, 100.