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基于自适应窗和形状自适应小波变换的SAR图像相干斑抑制
引用本文:凤宏晓.基于自适应窗和形状自适应小波变换的SAR图像相干斑抑制[J].红外与毫米波学报,2009,28(3):212-217.
作者姓名:凤宏晓
作者单位:西安电子科技大学,智能信息处理研究所和智能感知与图像理解教育部重点实验室,陕西,西安,710071
基金项目:国家自然科学基金,国家高技术研究发展计划(863计划),国家重点基础研究发展规划(973计划) 
摘    要:结合SAR图像空域的先验知识和小波域系数的特性,提出了一种新的SAR图像相干斑抑制算法.使用最近提出的局部多项式近似-置信区间交叉(local polynomial approximation-intersection of confidence intervals(LPA-ICI))构造自适应窗,寻找到与SAR图像中每个像素点相对应的同质区域,在每个同质区域内利用本文给出的快速形状自适应小波变换进行硬阈值收缩抑斑,最后根据本文提出的稀疏加权方法融合多个估计样本获得最终抑斑图像.实验结果表明本文提出的算法有着很好的抑斑性能,尤其是在去除重构图像中的"振铃"效应以及有效保留原始SAR图像中的点目标方面性能更突出.

关 键 词:SAR图像相干斑抑制  自适应窗  形状自适应小波变换  局部多项式近似-置信区间交叉  基于稀疏性的权值
收稿时间:1/9/2009 12:00:00 AM
修稿时间:1/9/2009 12:00:00 AM

SAR IMAGE DESPECKLING BASED ON ADAPTIVE WINDOW AND SHAPE ADAPTIVE - DISCRETE WAVELET TRANSFORM
fenghongxiao.SAR IMAGE DESPECKLING BASED ON ADAPTIVE WINDOW AND SHAPE ADAPTIVE - DISCRETE WAVELET TRANSFORM[J].Journal of Infrared and Millimeter Waves,2009,28(3):212-217.
Authors:fenghongxiao
Affiliation:Institute of Intelligent Information Processing and Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education
Abstract:Considering the prior knowledge of SAR image in spatial domain and the property of coefficients in wavelet domain, this paper presents a novel algorithm of SAR image despeckling: constructing an adaptive window and finding a uniform region for every pixel of SAR image by using Local Polynomial Approximation-Intersection of Confidence Intervals (LPA-ICI), implementing hard-threshold shrinkage with fast shape adaptive discrete wavelet transform proposed in this paper. At last, many despeckled samples are fused into a final despeckled SAR image according to the sparsity of regions, which is presented in this paper. Experiments show that the algorithm proposed in this paper has advanced despeckled performance. Especially, reconstructed image is absent from unpleasant ringing artifacts, and efficiently reserves point targets of original SAR image.
Keywords:SAR image despeckling  adaptive window  SA-DWT  Local Polynomial Approximation- Intersection of Confidence Intervals  weighting according to sparsity
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