Spectrum reconstruction of non-uniform sampling interference data via a deep neural network

Jun Cao; Song Liu; Zixin Wang; Weiping Wang; Xiaoyan Hu

doi:10.1117/12.3018543

30 April 2024 Spectrum reconstruction of non-uniform sampling interference data via a deep neural network

Jun Cao, Song Liu, Zixin Wang, Weiping Wang, Xiaoyan Hu

Proceedings Volume 13156, Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition; 1315619 (2024) https://doi.org/10.1117/12.3018543
Event: Sixth Conference on Frontiers in Optical Imaging Technology and Applications (FOI2023), 2023, Nanjing, JS, China

Abstract

The infrared digital Fourier transform spectrometer has several advantages, including small size, light weight, high stability, increased throughput, and enhanced spectral resolution, making it a valuable tool in the biomedical field. The data acquired by this instrument directly is interference data, and the required spectral data is obtained through a spectral recovery algorithm. The reconstruct spectral is determined by the acquired data quality and spectral recovery algorithm. However, the type of silicon photonics-based Fourier transform infrared spectrometers often encounter non-uniform optical path differences in collected interference data, due to the limitations in hardware design and manufacturing processes, leading to the spectral obtained by commonly used spectrum reconstruction methods inaccuracies. In this paper, a spectral recovery algorithm based on deep neural networks is proposed for the reconstruction of spectra from non-uniformly sampled interference data, Compared to other spectral recovery methods, the proposed method achieves better spectral angle (SA) and relative quality error (RQE) between the reconstructed spectra and ideal spectra.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jun Cao, Song Liu, Zixin Wang, Weiping Wang, and Xiaoyan Hu "Spectrum reconstruction of non-uniform sampling interference data via a deep neural network", Proc. SPIE 13156, Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition, 1315619 (30 April 2024); https://doi.org/10.1117/12.3018543

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