Inverse matrix iteration deconvolution method for spectrum resolution enhancement

Abstract

The invention discloses an inverse matrix iteration deconvolution method for spectrum resolution enhancement. The method comprises the following steps of 1, sequence convolution and a convolution square matrix; 2, convolution square matrix cumulative multiplication and convolution kernel function peak broadening; and step 3, peak resolution enhancement. An original function is multiplied by a deconvolution matrix to achieve the goal of narrowing the peak width; furthermore, a deconvolution unit matrix construction method is provided, the deconvolution calculation effect with expected precisionis realized; the method has the advantages that the calculation process is fast and controllable, the result is stable and accurate, the application range is wide, the method can be used for Raman, infrared and other molecular spectrums and can also be used for resolution enhancement of other spectrums with symmetrical peak types such as mass spectrums, nuclear magnetic resonance spectrums, XRD and XRF, and the Raman spectrums are taken as examples, so that implementation steps and implementation effects are given.

Description

technical field [0001] The invention relates to the field of spectral resolution enhancement, in particular to an inverse matrix iterative deconvolution method for spectral resolution enhancement. Background technique [0002] Spectroscopy, such as Raman, infrared, XRD, XRF and NMR, all require high resolution. The price of the instrument rises significantly with its resolution performance. Therefore, it has been expected to find a stable, accurate and extensive resolution enhancement algorithm in signal processing. [0003] The essence of resolution reduction is the result of instrument or other factors convolving with the real signal. In terms of instruments, such as slit, monochromaticity of light source or excitation source, spectrometer and detector performance, etc., other factors include temperature, pressure, Random broadening such as Brownian motion. Expressed in function, that is, the observation result is the result of the compound convolution of the real signal...

AI Technical Summary

Problems solved by technology

[0004] At present, the most stable and effective deconvolution method is the fast Fourier (FFT) deconvolution method. Based on this, various methods have been developed. The disadvantage of such methods is that they need to define accurate kernel functions, but finding The universal kernel function is difficult, which directly affects the universality and ease of use of the method

There are many factors affecting the real data, and the kernel function is complicated. In fact, it is very difficult to propose a universally applicable unified kernel function. It is also very difficult to solve the problem of resolution enhancement of various instruments in different occasions, and at the same time meet the requirements of stability, accuracy and wide range.

[0005] Aiming at the difficulty of finding the kernel function, the developed blind deconvolution method can effectively solve the problem of finding the kernel function, and can be used for deconvolution with non-strictly consistent results such as graphics and image resolution enhancement, but this method also brings repeatability of the results problems, making it difficult to meet spectroscopic requirements that require strict consistency and reproducible results

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