论文标题
使用离散希尔伯特转换的学习式矩阵表示从汽车光谱中提取拉曼信号
Raman Signal Extraction from CARS Spectra Using a Learned-Matrix Representation of the Discrete Hilbert Transform
论文作者
论文摘要
由于干扰非共振背景（NRB）而导致的相干反stokes拉曼散射（CAR）光谱中的扭曲对于定量分析至关重要。流行的计算方法，Kramers-Kronig关系和最大熵方法已显示出成功，但由于峰值在记录窗口以外的任何部分延伸的峰值可能会产生重大错误。在这项工作中，我们提出了一种学习的矩阵方法，用于使用Kramers-Kronig方法易于实施，快速和显着提高拉曼检索的准确性。
Removing distortions in coherent anti-Stokes Raman scattering (CARS) spectra due to interference with the nonresonant background (NRB) is vital for quantitative analysis. Popular computational approaches, the Kramers-Kronig relation and the maximum entropy method, have demonstrated success but may generate significant errors due to peaks that extend in any part beyond the recording window. In this work, we present a learned matrix approach to the discrete Hilbert transform that is easy to implement, fast, and dramatically improves accuracy of Raman retrieval using the Kramers-Kronig approach.
