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Infrared spectrum blind self-deconvolution method based on deep learning neural network

A technology of infrared spectrum and neural network, applied in the field of infrared spectrum blind self-deconvolution

Active Publication Date: 2021-08-24
NANJING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Adding full variation (TV) regularization can solve the problem of training overfitting

Method used

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  • Infrared spectrum blind self-deconvolution method based on deep learning neural network
  • Infrared spectrum blind self-deconvolution method based on deep learning neural network
  • Infrared spectrum blind self-deconvolution method based on deep learning neural network

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Embodiment Construction

[0045]The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0046] In this example, python and MATLAB are used as the implementation platforms.

[0047] The present invention passes such as figure 1 The shown method realizes the recovery of potentially clean infrared spectrum from the degraded infrared spectrum, and the blind infrared spectrum deconvolution method of the present invention and several other traditional infrared spectrum deconvolution methods are tested on the same data set Compared. The specific steps are as follows:

[0048] Step 1: Establish a generative network model G for blind self-deconvolution of infrared spectra x , G k

[0049] 1. Build a generative network G x

[0050] The blind self-deconvolutional neural network system proposed by the present invention includes an asymmetric autoencoder network with skip connections and...

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Abstract

The invention discloses an infrared spectrum blind self-deconvolution method based on a deep learning neural network; the method comprises the steps: inputting a degraded infrared spectrum into a constructed generative network model, and recovering a potential clean infrared spectrum; constructing a generative network model: establishing the generative network model; determining an infrared spectrum degradation formula, and adding a total variation regularization function to optimize the generative network model; using a joint optimization algorithm to update parameters including the original infrared spectrum x and the fuzzy kernel k; inputting the degraded infrared spectrum into a generative network model for iterative training; performing iterative training can be stopped until conditions are met, and a potential clean infrared spectrum and an error rate root-mean-square error RMSE, a correlation coefficient CC and a self-weighting correlation coefficient WCC of the potential clean infrared spectrum and an original infrared spectrum are obtained.

Description

technical field [0001] The invention relates to an infrared spectrum blind self-deconvolution method based on a deep learning neural network, and belongs to the technical field of infrared spectrum deconvolution processing. Background technique [0002] With the development of science and technology and the integration of various industries, infrared spectroscopy has become one of the most important tools for analyzing chemical structures. Infrared spectroscopy can not only be used to study the structure and chemical bonds of molecules, such as the determination of force constants and the criterion of molecular symmetry, but also can be used as a method to characterize and identify chemical species with high accuracy. In the past few decades, it has achieved many excellent results and has been applied in various fields, such as: liquid detection, drug detection, chemical structure analysis, biomaterials, medical images and food quality, etc. However, in the process of colle...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06K9/62
CPCG06N3/084G06N3/047G06F18/241Y02T10/40
Inventor 鞠婷朱虎邓丽珍程维文
Owner NANJING UNIV OF POSTS & TELECOMM
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