Parametric spectral signal restoration via maximum entropy constraint and its application

Hai Liu, Zhaoli Zhang, Sanya Liu, Jiangbo Shu, Tingting Liu
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引用次数: 3

Abstract

In this paper, we will propose a new framework which can estimate the desired signal and the instrument response function (IRF) simultaneously from the degraded spectral signal. Firstly, the spectral signal is considered as a distribution, thus, new entropy (called differential-entropy, DE) is defined to measure the distribution with a uniform distribution, which allows negative value existing. Moreover, the IRF is parametrically modeled as a Lorentzian function. Comparative results manifest that the proposed method outperforms the conventional methods on peak narrowing and noise suppression. The deconvolution IR spectrum is more convenient for extracting the spectral feature and interpreting the unknown chemical mixtures.
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基于最大熵约束的参数谱信号复原及其应用
在本文中,我们将提出一个新的框架,可以同时估计期望信号和仪器响应函数(IRF)从退化的频谱信号。首先,将谱信号视为一个分布,定义新熵(称为微分熵,DE)来度量均匀分布的分布,允许负值存在。此外,IRF被参数化建模为洛伦兹函数。对比结果表明,该方法在峰窄和噪声抑制方面优于传统方法。反褶积红外光谱更便于提取光谱特征和解释未知化学混合物。
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