自適應(yīng)調(diào)節(jié)濾波強度的SAR圖像非局部平均抑斑算法

朱磊; 李敬曼; 潘楊; 劉玉春; 胡曉

doi:10.11999/JEIT200099

自適應(yīng)調(diào)節(jié)濾波強度的SAR圖像非局部平均抑斑算法

doi: 10.11999/JEIT200099

朱磊^1, ,,
李敬曼¹,
潘楊¹,
劉玉春^{1, 2},
胡曉¹

1.
西安工程大學(xué)電子信息學(xué)院西安 710048
2.
周口師范學(xué)院機械與電氣工程學(xué)院周口 466001

基金項目: 國家自然科學(xué)基金(61971339)，陜西省重點研發(fā)計劃(2019GY-113)，西安市科技局創(chuàng)新引導(dǎo)計劃(201805030YD8CG14(6))

詳細(xì)信息

作者簡介:
朱磊：男，1979年生，教授，碩士生導(dǎo)師，研究方向為圖像處理、嵌入式系統(tǒng)應(yīng)用

李敬曼：女，1996年生，碩士生，研究方向為圖像處理

潘楊：女，1983年生，講師，研究方向為數(shù)字信號處理、聲場仿真與聲信號處理

劉玉春：男，1979年生，副教授，研究方向為信號與信號處理

胡曉：女，1993年生，碩士生，研究方向為圖像處理

通訊作者:
朱磊　zhulei791014@163.com

中圖分類號: TN911.73; TP751
計量
- 文章訪問數(shù): 796
- HTML全文瀏覽量: 460
- PDF下載量: 101
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2020-02-11
- 修回日期: 2020-09-09
- 網(wǎng)絡(luò)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: 為提升對SAR圖像乘性相干斑的抑制水平與邊緣保護性能，該文提出了一種可自適應(yīng)調(diào)節(jié)濾波強度(AFS)的SAR圖像非局部平均(NLM)抑斑新算法(AFS-NLM)。該算法利用Frost濾波圖像計算的局部均值與方差來改善SAR圖像場景參量的估計，形成了一種能更好刻畫SAR圖像同質(zhì)區(qū)與邊緣區(qū)的改進Kuan濾波系數(shù)。利用局部均值比與改進Kuan濾波系數(shù)分別作為新的相似性測量參量與自適應(yīng)衰減因子，構(gòu)建了一種更適應(yīng)SAR圖像乘性噪聲特性的改進NLM濾波。利用偏平滑參數(shù)與偏邊緣保護參數(shù)控制下的改進NLM濾波，分別替代經(jīng)典Kuan濾波模型中的像素局部均值與自身灰度值作為加權(quán)項，并采用由改進Kuan濾波系數(shù)構(gòu)建的自適應(yīng)調(diào)節(jié)因子對二者進行加權(quán)平均，從而形成了一種可自適應(yīng)調(diào)節(jié)濾波強度的加權(quán)濾波新模型。實驗表明，該文算法與近期多種先進算法相比，具有更好的相干斑抑制與邊緣保護性能。
- SAR圖像 /
- 相干斑抑制 /
- 自適應(yīng)濾波強度 /
- 非局部平均 /
- 改進Kuan濾波
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

圖 1 自適應(yīng)調(diào)節(jié)NLM濾波強度的SAR圖像抑斑新模型框圖

下載: 全尺寸圖片幻燈片

圖 2 Kuan濾波系數(shù)與改進Kuan濾波系數(shù)對比

下載: 全尺寸圖片幻燈片

圖 3 兩種方法估計的NLM濾波加權(quán)系數(shù)圖對比

下載: 全尺寸圖片幻燈片

圖 4 實驗測試用真實SAR圖像

下載: 全尺寸圖片幻燈片

圖 5 各算法對圖4兩幅真實SAR圖像的抑斑圖及其邊緣檢測圖對比

下載: 全尺寸圖片幻燈片

表 1 4種算法對真實SAR圖像抑斑參數(shù)比較

抑斑算法	${V_{\rm{ENL}}}$				${V_{\rm{EPI}}}$
抑斑算法	A區(qū)	B區(qū)	C區(qū)	D區(qū)	圖4(a)	圖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

下載: 導(dǎo)出CSV

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