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Raman spectrum often suffers from band overlap and Poisson noise. This paper presents a new blind Poissonian Raman spectrum reconstruction method, which incorporates the L0-sparse prior together with the total variation constraint into the maximum a posteriori framework. Furthermore, the greedy analysis pursuit algorithm is adopted to solve the L0-based minimization problem. Simulated and real spectrum experimental results show that the proposed method can effectively preserve spectral structure and suppress noise. The reconstructed Raman spectra are easily used for interpreting unknown chemical mixtures.

Band overlap and random noise exist widely when the spectra are captured using an infrared spectrometer, especially when the problems of instrument aging has become more and more serious recently. In this paper, via introducing the similarity of multiscales, a blind spectral deconvolution method is proposed. Considering similarity of the latent spectrum between different scales, it is used as a prior to constrain the estimated latent spectrum similar to pre-scale to reduce artifacts which is produced from deconvolution. Experiments indicate that the proposed method is able to obtain better performance than the state-of-the-art methods, and obtain satisfying deconvolution results with fewer artifacts. The recovered infrared spectra can easily extract the spectral features and recognize the unknown objects.

Band overlap and random noise exist widely when the spectra are captured using an infrared spectrometer, especially since the aging of instruments has become a serious problem. In this paper, via introducing the similarity of multiscales, a blind spectral deconvolution method is proposed. Considering that there is a similarity between latent spectra at different scales, it is used as prior knowledge to constrain the estimated latent spectrum similar to pre-scale to reduce artifacts which are produced from deconvolution. The experimental results indicate that the proposed method is able to obtain a better performance than state-of-the-art methods, and to obtain satisfying deconvolution results with fewer artifacts. The recovered infrared spectra can easily extract the spectral features and recognize unknown objects.