论文标题
基于一维光谱的奇异值分解及其在精确存储环质谱法中的应用
Denoising scheme based on singular-value decomposition for one-dimensional spectra and its application in precision storage-ring mass spectrometry
论文作者
论文摘要
这项工作涉及一维光谱的降噪，如果信号被添加剂白噪声损坏。提出的方法首先将噪声频谱映射到部分循环矩阵。根据矩阵的单数值分解，属于信号的组件是通过检查左单数矢量的总变化来确定的。之后，通过仅由信号成分组成的矩阵的低级别近似来重建平滑光谱。在其他现有的非参数方法中，提出的方法的脱氧作用被证明具有高度竞争性，包括移动平均值，小波收缩和总变化。此外，证明其在精确存储环质谱法中的适用方案被证明是相当多样化和吸引人的。
This work concerns noise reduction for one-dimensional spectra in the case that the signal is corrupted by an additive white noise. The proposed method starts with mapping the noisy spectrum to a partial circulant matrix. In virtue of singular-value decomposition of the matrix, components belonging to the signal are determined by inspecting the total variations of left singular vectors. Afterwards, a smoothed spectrum is reconstructed from the low-rank approximation of the matrix consisting of the signal components only. The denoising effect of the proposed method is shown to be highly competitive among other existing nonparametric methods, including moving average, wavelet shrinkage, and total variation. Furthermore, its applicable scenarios in precision storage-ring mass spectrometry are demonstrated to be rather diverse and appealing.
