A New Approach for Spectra Baseline Correction using Sparse Representation

Shujian Yu, Xinge You, Yi Mou, Xiubao Jiang, Weihua Ou, and Long Zhou


Spectra baseline correction, Sparse, Dictionary learning, KSVD algorithm


A new baseline correction algorithm for spectral signal based on sparse representation is proposed. Firstly, utilizing the training sample to obtain the dictionaries of both baseline and spectrum; secondly, establishing sparse representation model of spectral signal; thirdly, employing OMP algorithm to calculate the representation coefficients of spectral signal and finally, obtaining the spectral baseline from representation coefficients which are corresponded to the baseline dictionary. Then, the spectra baseline correction is completed by removing the baseline from original observed spectrum. Contrast experiment and quantitative analysis of corrected spectral signals are conducted and results show the highly efficiency and accuracy of the proposed algorithm.

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