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THz spectrum deconvolution with Total variation regularization

机译:具有总变化正则化的THz频谱反卷积

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Deconvolution has become one of the most used methods for improving spectral resolution, and blind deconvolution as a typical method has been researched widely. However, the predefined point spread function (PSF) used in blind deconvolution method is not known exactly in practice. In general, the PSF is estimated simultaneously from the observed spectrum, but it becomes difficult when the spectroscopic data are polluted by strong noise. In this paper, we present a deconvolution method used to improve the resolution of THz spectrum. In the method, the energy function is constructed, which includes the likelihood term, Total variation of spectrum term and L2 norm of the PSF term. The PSF is modeled as a parametric function combination with the a priori knowledge about the characteristics of the instrumental response. The spectrum and the parameter of PSF are obtained by minimizing the energy functional using alternate minimization approach. Experimental results are shown to demonstrate the efficiency of the proposed method used for THz spectrum.
机译:反卷积已成为提高频谱分辨率的最常用方法之一,作为一种典型方法,盲卷积已被广泛研究。但是,在实践中并不完全知道在盲反卷积方法中使用的预定义点扩展函数(PSF)。通常,从观察到的光谱中同时估计PSF,但是当光谱数据被强噪声污染时,将变得困难。在本文中,我们提出了一种用于提高太赫兹频谱分辨率的反卷积方法。该方法构造了能量函数,包括似然项,频谱项的总变化和PSF项的L2范数。将PSF建模为参数函数,并结合有关仪器响应特性的先验知识。 PSF的频谱和参数是通过使用替代最小化方法来最小化能量函数而获得的。实验结果表明,该方法可有效用于太赫兹频谱。

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