Super-resolution infrared spectroscopy for gas analysis using convolutional neural networks

Samar Elaraby; Yasser M. Sabry; Sherif M. Abuelenin

doi:10.1117/12.2571293

20 August 2020 Super-resolution infrared spectroscopy for gas analysis using convolutional neural networks

Samar Elaraby, Yasser M. Sabry, Sherif M. Abuelenin

Proceedings Volume 11511, Applications of Machine Learning 2020; 115110W (2020) https://doi.org/10.1117/12.2571293
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

Miniaturized Fourier transform infrared spectrometers serve important market needs in many applications such as gas analysis. The miniaturization comes at the cost of lower performance than benchtop instrumentation especially for the spectral resolution. Higher spectral resolution is needed for better identification of materials. This article presents a convolutional neural network (CNN) for enhancing the resolution of infra-red gas spectra for 3X resolution enhancement. The proposed network extracts a set of high-dimensional features from the input spectra and constructs high-resolution outputs by nonlinear mapping. The network was trained using synthetic noisy spectra of different resolutions of mixtures of a set of gases that are relevant to the gas industry. Results are presented for both synthetic and experimentally measured spectra.

Conference Presentation

Citation Download Citation

Samar Elaraby, Yasser M. Sabry, and Sherif M. Abuelenin "Super-resolution infrared spectroscopy for gas analysis using convolutional neural networks", Proc. SPIE 11511, Applications of Machine Learning 2020, 115110W (20 August 2020); https://doi.org/10.1117/12.2571293

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