Raman spectrum denoising method based on deep learning

A Raman spectroscopy and deep learning technology, applied in the field of optics and optical detection, can solve the problems of denoising, unable to automatically set denoising parameters, unable to accurately and efficiently Raman spectroscopy, etc., to achieve strong generalization ability and robustness , effects that are easy to extend and use

Pending Publication Date: 2022-04-29
山东捷讯通信技术有限公司
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Problems solved by technology

[0006] The purpose of the present invention includes two: (1) solve the problem that the current method cannot accurately and efficiently denoise the Raman spectrum; (2) solve the problem that the current method cannot automatically set the denoising parameters

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  • Raman spectrum denoising method based on deep learning
  • Raman spectrum denoising method based on deep learning
  • Raman spectrum denoising method based on deep learning

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Embodiment Construction

[0047] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0048] This example provides a Raman spectrum denoising method based on deep learning. In this method, a large amount of simulated Raman spectrum data is used for training, and a U-shaped deep learning model with an encoder and decoder structure is built to perform denoising on the Raman spectrum data. Noise removal. The invention can accurately, efficiently and automatically denoise the Raman spectrum, and can solve the problems of losing spectral information and relying on manual intervention to set parameters during the denoising process of the Raman spectrum.

[0049] The concrete steps of this method are as follows:

[0050] Step 1: Use the formula to simulate the Raman spectrum data, and use the Lorentz profile to fit the characteristic peaks of the pure Raman spectrum, such as figure 1 shown. Fit a noisy signal using Poisson noise and Gaussian...

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Abstract

The invention provides a Raman spectrum denoising method based on deep learning, which solves the problem that pure Raman spectrum information is easy to lose in the denoising process in the prior art, and input parameters are set by means of human intervention. The invention provides a deep learning network-based Raman spectrum denoising method, which comprises the following steps of: generating Raman spectrum data required by model training, and dividing the Raman spectrum data into a training set, a verification set and a test set; then, a U-Net Raman spectrum denoising model is established, the model is divided into an encoder network module and a decoder network module, feature extraction is performed on Raman spectrum data by using one-dimensional convolution in the encoder network, and an extracted feature spectrum is reconstructed and a pure spectrum is output in the decoder network. According to the method, noise of the Raman spectrum can be effectively removed, Raman spectrum information is well reserved, and accurate and reliable information is provided for further qualitative and quantitative analysis of the Raman spectrum. The method can be widely applied to the optical field.

Description

technical field [0001] The present invention relates to the field of optics, in particular to the field of optical detection technology, and specifically refers to a deep learning-based Raman spectrum denoising method. Background technique [0002] Raman spectroscopy is a technology proposed by Indian scientist Raman in the 1920s. The birth of lasers in the 1960s brought room for development in the application of Raman spectroscopy. Raman spectroscopy has been widely used as an effective detection and analysis method due to its rich material property information, non-destructive testing, and no need for sample preparation. [0003] Raman spectroscopy is an inelastic scattering spectrum produced by the molecular vibration of substances, which can perform qualitative and quantitative analysis of substances, so it has been widely used in many fields such as medical treatment and chemical industry. Since Raman spectroscopy is an extremely sensitive spectrum, which is easily af...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/048G06N3/045G06F2218/04G06F2218/08G06F18/214
Inventor 谷永辉刘昌军
Owner 山东捷讯通信技术有限公司
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