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Yang G, Guo Y H, Pu M B, et al. Miniature computational spectral detection technology based on correlation value selection[J]. Opto-Electron Eng, 2022, 49(10): 220130. doi: 10.12086/oee.2022.220130
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Miniature computational spectral detection technology based on correlation value selection
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Abstract
Benefiting from the advantages of small size, compact structure, and easy integration, miniature spectral detection technologies based on metasurfaces have been widely studied in recent years. However, the existing designs of the metasurfaces-based miniature spectral detection system usually lack the quantitative analysis of the relationship between the average correlation values of the metasurfaces transmission spectra and the reconstruction quality. The random selection method used in the existing design process cannot guarantee the optimal reconstruction quality. This paper quantitatively analyzes the relationship between the average correlation value of the metasurfaces transmission spectra and reconstruction quality, and proposes a design methodology for miniature spectral detection based on metasurfaces. In addition, this paper also verifies the spectral properties of the metasurfaces-based miniature spectral detection technology. Compared with the random selection design methodology, the proposed methodology can improve the reconstruction fidelity of broadband spectral and image signals.-
Keywords:
- quantitative analysis /
- metasurfaces /
- methodology /
- miniature spectral detection
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References
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Overview
Spectral imaging detection technology has been widely used in many fields, such as remote sensing, medical diagnosis, food safety testing, environmental monitoring, and other fields due to its advantages of accurate and non-contact detection. However, conventional spectral imaging systems usually suffer from the large volume, long sampling time, and low energy efficiency. Metasurface is an artificial two-dimensional material that can flexibly control the amplitude, phase and spectrum of electromagnetic waves. Metasurfaces have been used in spectral detection, holography, metalens, and other fields due to its compact structure and the capacity to flexibly control the electromagnetic waves. Benefiting from the advantages of small size, compact structure, and easy integration, miniature spectral detection technologies based on metasurfaces have been widely studied in recent years. The miniature spectral detection systems usually utilize the broadband spectral properties of metasurfaces and compressive sensing algorithms to achieve computational spectral imaging detection with lightweight. However, the existing designs of the metasurfaces-based miniature spectral detection system usually lack the quantitative analysis of the relationship between the average correlation values of the metasurfaces transmission spectra and the reconstruction quality. The random selection method used in the existing design process cannot guarantee the optimal reconstruction quality. Different from the traditional methodology of using the maximum linear independence criterion to select the broadband filters, this paper quantitatively analyzes the relationship between the average correlation value of the metasurfaces transmission spectra and reconstruction quality, and proposes a methodology for miniature spectral detection based on metasurfaces, which provides a route for the subsequent design and optimization of the metasurfaces. In order to verify the advantages of the proposed methodology, ten broadband spectra and image spectra were selected from many spectra. Compared with the random selection design methodology, the proposed methodology can improve the reconstruction fidelity of broadband spectral and image signals. The fidelity of the broadband spectral reconstruction can be increased by 13.17%, and the reconstruction fidelity of the image spectral signals has also been improved to a certain extent. In addition, this paper also verifies the spectral properties of the metasurfaces-based miniature spectral detection technology, showing that the system has good reconstruction effect for broadband, narrowband and image spectral signals, and has the advantages of compact structure and small volume.
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Yang G, Guo Y H, Pu M B, et al. Miniature computational spectral detection technology based on correlation value selection[J]. Opto-Electron Eng, 2022, 49(10): 220130. doi: 10.12086/oee.2022.220130
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Figure 1.
Miniature spectral detection. (a) Schematic diagram of the working principle; (b) Schematic diagram of numerous micro-spectrometers; (c) Schematic diagram of a single micro-spectrometer and transmission spectrum of a metasurface
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Figure 2.
Design of the metasurfaces. (a) The unit cell of the metasurfaces; (b) Schematic diagram of a single micro-spectrometer; (c) Schematic diagram of the selection of metasurfaces according to different average correlation value intervals; (d) Transmission spectra of different patterns of the metasurfaces
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Figure 3.
Flow chart of our proposed methodology and traditional methodology
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Figure 4.
The reconstruction fidelity produced by different metasurfaces selection design methodologies in Table 1. (a) Spectrum 1~5 in Table 1; (b) Spectrum 6~10 in Table 1; (c) The reconstruction fidelity produced by different metasurfaces selection design methodologies under spectrum5; (d) The reconstruction fidelity produced by different metasurfaces selection design methodologies under spectrum10
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Figure 5.
Spectral characteristic simulation verification. (a) Incident spectrum and the reconstructed spectrum with a central wavelength of 560 nm and a bandwidth of 1.8 nm; (b) Enlarged images around the central wavelength in Fig. 5(a); (c) Spectral resolution simulation verification with a central wavelength interval of 2 nm; (d) Spectral resolution simulation verification with a central wavelength interval of 3 nm; (e) Reconstruction spectrum and reconstruction fidelity of broadband spectrum 1 under different number of structures M; (f) Reconstruction spectrum and reconstruction fidelity of broadband spectrum 2 under different number of structures M
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Figure 6.
Image spectral signals perception verification. (a), (b) Original and reconstructed image spectral signals respectively[46]; (c) Reconstruction fidelity of spectral signals generated by different metasurface design methods under different color blocks. The reconstructed spectrum 1 is produced from the metasurface structures selected using the average correlation value interval [0.1~0.3], and the reconstructed spectrum 2 is produced from the randomly selected metasurface structures
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