Independent component analysis based blind deconvolution of spectroscopic data

  • Authors:

  • Jinghe Yuan;Shengjiang Chang;Ziqiang Hu;Yanxin Zhang

  • Affiliations:

  • Institute of Science and Technology for Opto-electron Information, Yantai University, Yantai, China;Institute of Modern Optics, Nankai University, Key Laboratory of Opto-electronics Information Technical Science, Tianjin, EMC, China;Institute of Science and Technology for Opto-electron Information, Yantai University, Yantai, China;Institute of Modern Optics, Nankai University, Key Laboratory of Opto-electronics Information Technical Science, Tianjin, EMC, China

  • Venue:
  • ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
  • Year:
  • 2006

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Abstract

The spectroscopic data recorded by dispersion spectrophotometer are usually degraded by the response function of the instrument. To improve the resolving power, double or triple cascade spectrophotometer, and narrow slits have been employed, but the total flux of the radiation available decreases accordingly, resulting in a lower signal-to-noise ratio (SNR) and a longer measure time. However, the spectral resolution can be improved by mathematically removing the effect of the instrument response function. An independent component analysis based algorithm is proposed to blindly deconvolve the measured spectroscopic data. The true spectrum and the instrument response function are estimated simultaneously. In the preprocessing stage, the noise can be reduced in some degree. Experiments on some real measured spectroscopic data demonstrate the feasibility of this method.