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基于正余弦分解的两分段自适应非局部均值滤波方法OA

A Two-Stage Adaptive Non-Local Mean Filtering Method Based on Sine-Cosine Decomposition

中文摘要英文摘要

为解决包裹相位图中存留的散斑噪声问题,文中提出了一种基于正余弦分解的两分段自适应非局部均值滤波方法.该方法通过两次改进衰减参数的大小和相似性度量的方式实现了算法的自适应化.利用该方法对包裹相位图的正余弦分量去噪,去噪后利用反正切运算获取干净的包裹相位,对该相位进行解包裹运算.实验和仿真结果表明,所提方法既有效去除了包裹相位图中的噪声,也保留了相位图中的边缘信息.相比于分别使用SCA(Sine Cosine Algo-rithm)方法和BM3D(Block-Matching and3Dfiltering)方法,通过所提方法去噪后的图像等效视数(Equivalent Number of Looks,ENL)最大,散斑抑制指数(Speckle Suppression Index,SSI)最小,且均方误差提升了约两倍,说明所提方法有效去除了包裹相位中的噪声,提高了相位解包裹的精度.

In order to solve the problem of speckle noise in the wrapped phase diagram,a two-stage adaptive non-local mean filtering method based on sine and cosine decomposition is proposed.The proposed method realizes the adaption of the algorithm by improving the size and similarity measurement of the attenuation parameters twice.This method is used to denoise the sine and cosine components of the wrapped phase diagram.After denoising,the inverse tangent operation is used to obtain the clean wrapped phase,and unwrapping operation is carried out on the phase.The experimental and simulation results show that the proposed method not only effectively removes the noise in the wrapped phase diagram,but also preserves the edge information in the phase diagram.Compared with SCA(Sine Cosine Algorithm)method and BM3D(Block-Matching and 3D filtering)method,ENL(Equivalent Number of Looks)and SSI(Speckle Suppression Index)of the image denoised by the proposed method are the largest and the smallest,and the mean square error is increased by about two times.These results reveal that the proposed method can effectively remove the noise in the wrapped phase and improve the accuracy of phase unwrapping.

孙玉娟;王亚伟;汤馥睿;耿芫;李雨晨;徐媛媛

江苏大学 物理与电子工程学院,江苏 镇江 212013

物理学

包裹相位散斑噪声正余弦分解两分段相似性度量自适应化非局部均值相位解包裹

wrapped phasespeckle noisesine cosine decompositiontwo sectionssimilarity measureadap-tivenon-local meanphase unwrapping

《电子科技》 2024 (007)

81-88 / 8

国家自然科学基金(11874184)National Natural Science Foundation of China(11874184)

10.16180/j.cnki.issn1007-7820.2024.07.011

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