| 非子采样Contourlet变换系数统计建模及图像去噪应用 |
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| 引用本文： | 牛彦敏,王旭初.非子采样Contourlet变换系数统计建模及图像去噪应用[J].激光与光电子学进展,2010(5). |
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| 作者姓名： | 牛彦敏 王旭初 |
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| 作者单位： | 重庆师范大学信息科学与工程学院;重庆大学光电技术及系统教育部重点实验室; |
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| 基金项目： | 重庆市自然科学基金(CSTC2009BB3192);;重庆师范大学基金(07XLQ09)资助课题 |
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| 摘 要： | 融合拉普拉斯分布及广义高斯分布模型对非子采样Contourlet变换(NSCT)系数进行了统计建模分析。研究发现,NSCT作为平移不变Contourlet变换,系数在不同尺度和方向上均存在较大冗余,在广义高斯分布性等方面需引入参数加以约束。根据建立的统计模型进行了医学图像去噪实验。结果表明,和Contourlet及NSCT软硬阈值去噪等比较,该建模方法提高了噪声估计精度,增加了峰值信噪比,改善了图像视觉效果。
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| 关 键 词： | 图像处理 Contourlet变换 高斯混合模型 图像去噪 |
Statistical Modeling of Nonsubsampled Contourlet Transform Coefficients and Its Application to Image Denoising |
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| Niu Yanmin Wang Xuchu College of Information Science , Engineering,Chongqing Normal University,Chongqing ,China Key Laboratory of Optoelectronic Technologies , Systems,Ministry of Education,College of Optoelectronic Engineering,Chongqing University,Chongqing ,China.Statistical Modeling of Nonsubsampled Contourlet Transform Coefficients and Its Application to Image Denoising[J].Laser & Optoelectronics Progress,2010(5). |
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| Authors: | Niu Yanmin Wang Xuchu College of Information Science Engineering Chongqing Normal University Chongqing China Key Laboratory of Optoelectronic Technologies Systems Ministry of Education College of Optoelectronic Engineering Chongqing University Chongqing China |
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| Affiliation: | Niu Yanmin1 Wang Xuchu2 1 College of Information Science , Engineering,Chongqing Normal University,Chongqing 400030,China 2Key Laboratory of Optoelectronic Technologies , Systems,Ministry of Education,College of Optoelectronic Engineering,Chongqing University,Chongqing 400044,China |
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| Abstract: | A Laplace and generalized Gaussian mixture distribution-based method is proposed to explore the nonsubsampled Contourlet transform (NSCT) coefficients. The investigating result reveals that the NSCT, as a shift-invariant contourlet transform, obtains redundant coefficients in each scale and each direction, and its coefficients differ from those of Contourlet transform in aspects of general Gaussian distribution. The regularized parameters should be introduced to generalized Gaussian distribution model to en... |
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| Keywords: | image processing Contourlet transform generalized Gaussian distribution model image denoising |
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