Abstract

Raman spectroscopy often suffers from the problems of band overlap and random noise. In this work, we develop a nonlocal low-rank regularization (NLR) approach toward exploiting structured sparsity and explore its applications in Raman spectral deconvolution. Motivated by the observation that the rank of a ground-truth spectrum matrix is lower than that of the observed spectrum, a Raman spectral deconvolution model is formulated in our method to regularize the rank of the observed spectrum by total variation regularization. Then, an effective optimization algorithm is described to solve this model, which alternates between the instrument broadening function and latent spectrum until convergence. In addition to conceptual simplicity, the proposed method has achieved highly competent objective performance compared to several state-of-the-art methods in Raman spectrum deconvolution tasks. The restored Raman spectra are more suitable for extracting spectral features and recognizing the unknown materials or targets.

© 2018 Optical Society of America

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