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基于截断奇异值分解和级联多重网格技术的光子相关光谱反演

Inversion of photon correlation spectroscopy based on truncated singular value decomposition and cascadic multigrid technology.

作者信息

Wang Yajing, Shen Jin, Wei Liu, Dou Zhenhai, Gao Shanshan

机构信息

College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, China.

出版信息

Appl Opt. 2013 Apr 20;52(12):2792-9. doi: 10.1364/AO.52.002792.

Abstract

For the low accuracy of single-scale inversion method in photon correlation spectroscopy technology, a cascadic multigrid (CMG)-truncated singular value decomposition (TSVD) inversion method that combines the TSVD regularization with CMG technology is proposed. This method decomposes the original problem into several subproblems in different scale grid space. According to the particle sizes inverted from the coarsest scale to the finest scale, the solution of an original inversion problem can be obtained. For the inversion of each subproblem, TSVD method is used. The simulation and experimental data were respectively inverted by TSVD and CMG-TSVD methods. The inversion results demonstrate that the CMG-TSVD method has higher accuracy, more strong noise immunity and better smoothness than the TSVD method.

摘要

针对光子相关光谱技术中单一尺度反演方法精度较低的问题,提出了一种将截断奇异值分解(TSVD)正则化与级联多重网格(CMG)技术相结合的CMG - TSVD反演方法。该方法在不同尺度网格空间中将原问题分解为若干子问题,根据从最粗尺度到最细尺度反演得到的粒径,可获得原反演问题的解。对于每个子问题的反演,采用TSVD方法。分别用TSVD方法和CMG - TSVD方法对模拟数据和实验数据进行反演。反演结果表明,CMG - TSVD方法比TSVD方法具有更高的精度、更强的抗噪声能力和更好的平滑性。

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