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Improving low-resolution gas-mixture absorption spectra using neural networks

Skiba V.E. - National Research Tomsk State University, Tomsk, Russia;
Vrazhnov D.A. - National Research Tomsk State University, Tomsk, Russia, Institute of Strength Physics and Materials Science of SB RAS, Tomsk, Russia;
Prischepa V.V. - National Research Tomsk State University, Tomsk, Russia;
Miroshnichenko M.B. - Institute of Strength Physics and Materials Science of SB RAS, Tomsk, Russia;

Abstract

An important role in component analysis with spectral methods has a spectral resolution of used tools. The most useful and perspective methods to improve spectral resolution is decreasing of impulse response function (IRF) and improving resolution using superresolution (SR) reconstruction methods. We have analyzed different types of neural networks (convolution neural network, multilayered perceptron) for improving the spectral resolution of initial absorption spectra. The used approach is based on an association of a high-resolution and a low-resolution spectrum. The latter was constructed from high-resolution spectra to which IRF and some random noise were added. High-resolution spectra were generated using the HITRAN database. Most optimal architectures of neural networks to improve spectral resolution were defined.

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Skiba Victor
National Research Tomsk State University
Russian Federation

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