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http://dx.doi.org/10.9766/KIMST.2021.24.4.374

Denoising Autoencoder based Noise Reduction Technique for Raman Spectrometers for Standoff Detection of Chemical Warfare Agents  

Lee, Chang Sik (School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
Yu, Hyeong-Geun (School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
Park, Jae-Hyeon (School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
Kim, Whimin (School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
Park, Dong-Jo (School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
Chang, Dong Eui (School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
Nam, Hyunwoo (The 4th Research and Development Institute, Agency for Defense Development)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.24, no.4, 2021 , pp. 374-381 More about this Journal
Abstract
Raman spectrometers are studied and developed for the military purposes because of their nondestructive inspection capability to capture unique spectral features induced by molecular structures of colorless and odorless chemical warfare agents(CWAs) in any phase. Raman spectrometers often suffer from random noise caused by their detector inherent noise, background signal, etc. Thus, reducing the random noise in a measured Raman spectrum can help detection algorithms to find spectral features of CWAs and effectively detect them. In this paper, we propose a denoising autoencoder for Raman spectra with a loss function for sample efficient learning using noisy dataset. We conduct experiments to compare its effect on the measured spectra and detection performance with several existing noise reduction algorithms. The experimental results show that the denoising autoencoder is the most effective noise reduction algorithm among existing noise reduction algorithms for Raman spectrum based standoff detection of CWAs.
Keywords
Raman Spectroscopy; Noise Reduction; Standoff Detection; Chemical Warfare Agent; Hyperspectral Spectrometer;
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