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Joint sparse representation and denoising method for Raman spectrum
Author(s) -
Fang Zheng,
Tao Yu,
Wang Wen,
Zhang Wenxin,
Duan Lingfeng,
Liu Ying,
Yan Changchun,
Qu Lulu,
Han Caiqin
Publication year - 2018
Publication title -
journal of raman spectroscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.748
H-Index - 110
eISSN - 1097-4555
pISSN - 0377-0486
DOI - 10.1002/jrs.5485
Subject(s) - raman spectroscopy , sparse approximation , matching pursuit , noise (video) , noise reduction , signal (programming language) , representation (politics) , joint (building) , limit (mathematics) , signal to noise ratio (imaging) , computer science , pattern recognition (psychology) , spectrum (functional analysis) , artificial intelligence , algorithm , acoustics , materials science , physics , optics , mathematics , compressed sensing , mathematical analysis , engineering , architectural engineering , politics , law , political science , image (mathematics) , programming language , quantum mechanics
Abstract A new method based on joint sparse representation is developed to recover the peak information from high‐noise Raman signal. This method used the sparsity of Raman spectrum to recover the signals and preserve its useful peak information. The peak information is then reconstructed by using an orthogonal matching pursuit algorithm. The joint sparse representation method is found to be an effective approach to analyze the Raman spectrum, especially Raman spectrum that have high noise, thus improving the detection limit of Raman spectroscopy. Experimental results demonstrate that this approach is better than other approaches in case of low signal‐to‐noise ratios.