基于自适应差分滤波的干涉图基线校正方法

基线校正是傅里叶变换成像光谱仪光谱反演的重要环节，因为干涉曲线切趾、相位校正等光谱反演预处理步骤均需要在基线校正完成后进行。提出了一种用于基线校正的自适应差分滤波方法。该方法使用迭代算法动态调整加权均值滤波窗口。仿真结果表明，该滤波方法对直流趋势项的滤除更为彻底。利用实验室仪器获取的紫外潜指纹残留物光谱数据进行分析，结果说明，在仪器工作光谱范围内，使用该方法进行基线校正后得到的光谱曲线与有效光谱曲线基本一致。该方法无需提前选择滤波窗口，具有自适应性。并且基于均值滤波算法的自适应差分滤波方法计算流程简单，迭代效率较高。

Abstract

Baseline correction is an important step of spectral retrieving procedure for Fourier transform imaging spectrometer data processing, because preprocessing steps of spectral retrieving procedure, such as interference curve apodization and phase correction, need to be implemented after baseline correction is completed. A selfadaptive differential filtering method for baseline correction is presented. This method uses an iterative algorithm for dynamically adjusting its weighted mean filter window. The simulation results demonstrate that the filtering method can filter out the direct current trend more thoroughly. Analysis of the ultraviolet spectral data of latent fingerprint residues obtained by instrument in laboratory is made. The results indicate that within the working spectrum range of the instrument, the spectra obtained by the proposed baseline correction method are substantially consistent with the actual spectra. Since the method does not require a pre-selected mean filter window, it is selfadaptive. Moreover, the self-adaptive differential filtering method based on mean filter algorithm is simple and has high efficient iteration.

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基于自适应差分滤波的干涉图基线校正方法

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