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Adaptive total variation-based spectral deconvolution with the split Bregman method

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成果类型:
期刊论文
作者:
Liu, Hai;Liu, Sanya;Zhang, Zhaoli*;Sun, Jianwen;Shu, Jiangbo
通讯作者:
Zhang, Zhaoli
作者机构:
[Zhang, Zhaoli; Liu, Sanya; Shu, Jiangbo; Liu, Hai; Sun, Jianwen] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构:
[Zhang, Zhaoli] C
Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Algorithms;Analytical techniques;Deconvolution;Inverse problems;Mathematical methods;Raman spectroscopy
期刊:
Applied Optics
ISSN:
1559-128X
年:
2014
卷:
53
期:
35
页码:
8240-8248
基金类别:
Program for New Century Excellent Talents in UniversityProgram for New Century Excellent Talents in University (NCET) [NCET-11-0654]; National Key Technology Research and Development ProgramNational Key Technology R&D Program [213BAH72B01, 2013BAH18F02]; Scientific R & D Project of State Education Ministry and China Mobile [MCM20121061]; National Social Science Fund of China [14BGL131]; Chinese Ministry of EducationMinistry of Education, China
机构署名:
本校为第一且通讯机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
Spectroscopic data often suffer from common problems of band overlap and noise. This paper presents a maximum a posteriori (MAP)-based algorithm for the band overlap problem. In the MAP framework, the likelihood probability density function (PDF) is constructed with Gaussian noise assumed, and the prior PDF is constructed with adaptive total variation (ATV) regularization. The split Bregman iteration algorithm is employed to optimize the ATV spectral deconvolution model and accelerate the speed of the spectral deconvolution. The main advantage of this algorithm is that it can obtain peak struc...

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