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环形向量非局部SAR图像降噪算法 被引量：2

Circular-Vector Based Non-Local SAR Image Denoising Algorithm
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摘要 提出一种基于环形向量的非局部SAR图像降噪算法.根据像素点的主方向提取环形向量,计算环形向量各自的特征向量.基于特征向量计算相似度权重,该方法的时间复杂度明显优于NL-Means的矩形模板匹配算法,且相似点匹配具有旋转不变性.通过仿真实验验证了该算法的计算速度和旋转不变性能,匹配效果明显优于NL-Means,降噪结果的峰值信噪比和结构相似度优于BM3D、BLS-GSM等主流降噪算法. A circular-vector based non-local SAR image denoising algorithm was proposed. Circular vectors were abstracted according to the main direction of the central pixel. Feature vectors were then calculated and the weights of similarity were obtained based on these feature vectors. The execution time and quality of rotation invariance were tested. The results show that the time complexity is significantly lower than the patch-based similarity matching algorithm in NL-Means, and the rotation invariance is also maintained. Experiments have verified that the proposed algorithm shows better similarity matching results comparing to NL-Means and competitive denoising results on PSNR and SSIM against currently state-of-the-art denoising algorithms, such as BM3D, BLS-GSM.
作者 蔡雨辰 赵保军 唐林波
机构地区 北京理工大学信息与电子学院
出处 《北京理工大学学报》 EI CAS CSCD 北大核心 2012年第11期1174-1178,共5页 Transactions of Beijing Institute of Technology
基金 国家"八六三"计划项目(2011SQ8012321)
关键词 SAR图像降噪 非局部平均 环形向量 旋转不变性 SAR image denoising non-local means circular vector rotation invariance
分类号 TN919.8 [电子电信—通信与信息系统]
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参考文献9

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同被引文献12

  • 1郦苏丹,李广侠.结合多尺度边缘检测的SAR结构邻域滤波方法[J].电子与信息学报,2006,28(8):1480-1484. 被引量：6
  • 2薄华,马缚龙,焦李成.基于免疫算法的SAR图像分割方法研究[J].电子与信息学报,2007,29(2):375-378. 被引量：6
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  • 5Babacan S D, Luessi M, Spinoulas L, et al. Compressive passive millimeter-wave imaging[C]//Pro- ceedings of the 18th IEEE International Conference on Image Processing (ICIP). Brussels~ IEEE, 2011.. 2705 - 2708.
  • 6Buades A, Coll B, Morel J M. A non-local algorithm for image denoising [C] // Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IS. 1. ]: IEEE, 2005:60 - 65.
  • 7Mahmoudi M, Sapiro G. Fast image and video denoising via non-local means of similar neighborhoods[J]. IEEE Signal Processing Letters, 2005,12 : 839 - 842.
  • 8Feng X G, Milanfar P. Multiscale principal components analysis for image local orientation estimation[C]//Pro- ceedings of Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers. Pacific Grove: IEEE, 2002,1 ..478 - 482.
  • 9Liu X, Tanaka M, Okutomi M. Noise level estimation using weak textured patches of a single noisy image[C~ // Proceedings of 2012 19th IEEE International Conference on Image Processing (ICIP). Orlando: IEEE, 2012:665 - 668.
  • 10黄石生,朱炬波,谢美华.基于变分的SAR图像目标特征增强方法[J].红外与毫米波学报,2010,29(5):392-396. 被引量：4

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北京理工大学学报
2012年 第11期

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