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作 者:吴建宁 石满红 兴志[3] WU Jianning;SHI Manhong;XING Zhi(Department of Electrical Engineering,Nanjing Technical Vocational College,Nanjing 210019,China;College of Information&Network Engineering,Anhui Science and Technology University,Fengyang 233100,China;Nanjing College of Information Technology,Nanjing 210023,China)
机构地区:[1]南京高等职业技术学校电气工程系,江苏南京210019 [2]安徽科技学院信息与网络工程学院,安徽凤阳233100 [3]南京信息职业技术学院,江苏南京210023
出 处:《红外技术》2018年第8期798-804,共7页Infrared Technology
基 金:江苏省自然科学基金项目(BK20151574);安徽省自然基金资助项目(1508085MC55);安徽省教育厅自然科学重点项目(KJ2016A174);安徽科技学院校级项目(ZRC2016499)
摘 要:为了有效去除图像噪声并保留更多图像细节信息,提出一种结合PDTDFB变换域各项异性双变量拉普拉斯模型和非局部均值滤波的自适应图像去噪算法。首先分析了PDTDFB变换系数的分布特点,使用各项异性双变量拉普拉斯模型作为其父子系数相关性的先验分布,在贝叶斯去噪框架下推导出闭式形式的各项异性双变量阈值函数,然后对估计的变换系数进行逆PDTDFB变换得到初步去噪图像,最后使用非局部均值滤波对其进行平滑处理。实验结果显示:本文所提算法去噪效果明显,与一些经典算法相比,本文方法在主客观上皆取得了有竞争力的结果。This paper presents a new image denoising algorithm to effectively remove noise while retaining the important marginal and detail information of images.It is based on a combination of the anisotropic bivariate Laplacian model in the pyramidal dual-tree directional filter bank transform(PDTDFB)domain and a non-local means filter(NLM)in the spatial domain.First,the distribution characteristics of the PDTDFB transform coefficients are analyzed.Then,the coefficients are modeled as an anisotropic bivariate Laplacian model considering the child-parent statistical dependency between the PDTDFB coefficients.With this statistical model,a closed-form anisotropic bivariate shrinkage function is derived in the framework of the Bayesian theory.Second,an inverse PDTDFB transform is performed to obtain the initial denoised image.Finally,NLM is used to smooth the initial denoised image.The simulation results show that the proposed method provides promising results and is competitive with the classical denoising results reported in the literature both in terms of peak signal-to-noise ratio and visual quality.
关 键 词:图像去噪 PDTDFB变换 双变量拉普拉斯模型 非局部均值滤波
分 类 号:TP751.1[自动化与计算机技术—检测技术与自动化装置]
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