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

V. E. Skiba; D. A. Vrazhnov; V. V. Prischepa; M. B. Miroshnichenko

doi:10.1117/12.2580678

17 November 2020 Improving low-resolution gas-mixture absorption spectra using neural networks

V. E. Skiba, D. A. Vrazhnov, V. V. Prischepa, M. B. Miroshnichenko

Proceedings Volume 11582, Fourth International Conference on Terahertz and Microwave Radiation: Generation, Detection, and Applications; 115821C (2020) https://doi.org/10.1117/12.2580678
Event: Fourth International Conference on Terahertz and Microwave Radiation: Generation, Detection, and Applications, 2020, Tomsk, Russian Federation

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. Highresolution spectra were generated using the HITRAN database. Most optimal architectures of neural networks to improve spectral resolution were defined.

Citation Download Citation

V. E. Skiba, D. A. Vrazhnov, V. V. Prischepa, and M. B. Miroshnichenko "Improving low-resolution gas-mixture absorption spectra using neural networks", Proc. SPIE 11582, Fourth International Conference on Terahertz and Microwave Radiation: Generation, Detection, and Applications, 115821C (17 November 2020); https://doi.org/10.1117/12.2580678

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