SUBPIXEL SCANNING FOR SPECTRUM IMAGE ANALYSIS

DOI: https://doi.org/10.15407/rpra29.01.038

S. A. Yegorov, V. I. Lutsenko, A. D. Yegorov, V. A. Yegorov, I. E. Sinelnikov

Abstract


Subject and Purpose. In this paper, an algorithm is developed to improve the spectrum image resolution in the atomic emission spectrum registration using multi-element detectors and taking multiple exposures with image shifting by a fractional part of a pixel.

Methods and Methodology. Mathematical modeling techniques are employed in the coordinate representation dictated by the prior information. The modeling results are checked by experiment.

Results. The importance of the resolution value of spectral image registration systems and its impact on the quality of the obtained results have been highlighted. The spectral informativeness of the regularly (matrix- or linearly) structured multi-element detectors has been evaluated against the irregular-structure sensors, such as photographic emulsions. As it has been found that the process of pixel size reduction is demanding, an alternative approach through subpixel image shifting has been proposed. Unlike the case of the corresponding instruments implemented as a rule in the frequency domain, here the relevant theoretical problem reduces to the over- determined system of linear equations. The proposed solution algorithm employs the least squares method having regard to the prior data specificity posed by the isolation of the spectral lines and a smooth flow of the background between them. The digital simulation results and the real experimental evidence from the atomic emission spectral analysis have been presented, illustrating the algorithm operation. The experimental research was carried out for one-dimensional spectrum images registered with linear charge-coupling photodetectors. The so gained resolution was twice the spatial resolution of the photodetector.

Conclusions. It has been demonstrated that controlled image shifting not only improves the photometry accuracy but also significantly enhances the detector resolution. The general comparisons of the theory with the experiment have proved the feasibility of bringing the proposed methodology into atomic emission analysis.

Keywords: image processing, image registration with shifting, multielement sensors, spectral analysis, least squares method, subpixel resolution

Manuscript submitted 21.06.2023

Radio phys. radio astron. 2024, 29(1): 038-045

REFERENCES   

1. Yuu, F. T. S., 1979. Introduction to the theory of diffraction, information processing and holography. Moscow: Sov. Radio Publ. (in russian).

2. Peisakhson, I. V., 1975. Optics of spectral instruments. 2nd ed. Leningrad: Mashinostroenie Publ. (in Russian).

3. Maksutov, D. D., 1979. Astronomical optics. Leningrad: Nauka Publ. (in Russian).

4. Zhang, X. F., Huang, W., Xu, M.F., Jia, S.Q., Xu, X.R., Li, F.B., Zheng, Y.D., 2019. Super-resolution imaging for infrared micro- scanning optical system. Opt. Express, 27(5), pp. 7719—7737. DOI: https://doi.org/10.1364/OE.27.007719

5. Chen, J., Li, Y., Cao, L., 2021. Research on region selection super resolution restoration algorithm based on infrared micro- scanning optical imaging model. Sci Rep. 11, 2852. DOI: https://doi.org/10.1038/s41598-021-82119-1

6. Blazhevich, S. V., Vintaev, V. N., Ushakova, N. N., 2010. Image synthesis with enhanced resolution on the basis of subpixel scanning.
Sovr. Probl. DZZ Kosm., 7(2), pp. 9—13 (in Russian).

7. Peleg, S., Keren, D., Schweitzer, L., 1987. Improving image resolution using subpixel motion. Pattern Recognit. Lett., 5(3), pp. 223— 226. DOI: https://doi.org/10.1016/0167-8655(87)90067-5

8. Gross, D., 1986. Super-resolution from subpixel shifted pictures. Master’s thesis. Tel Aviv University.

9. Selyutina, E. S., Blazhevich, S. V., 2014. Enhancing the resolution of digital images using subpixel scanning. Nauchnyie vedomosti Belgorodskogo gosudarstvennogo universiteta. Mathematics. Physics, 5, pp. 186—190 (in Russian).

10. Shelpakova, I. R., Garanin, V. G., Chanysheva, T. A., 1998. Analytical Capabilities of a Multichannel Emission Spectrum Analyzer (MAES) in Spectral Analysis. Analytics and control, 1, pp. 33—40 (in Russian).

11. Yegorov, A. D., Yegorov, V. A., Yegorov, S. A., 2009. Subpixel Detection of Spectrum Images by Photodiode Structures. Radio Phys. Radio Astron., 14(1), pp. 77—83.

12. Lanczohs, K., 1961. Practical methods of applied analysis: Reference guide. Moscow: Fizmatgiz Publ. (in Russian).

13. Frieden, B. R., 1979. Image Enhancement and Restoration. In: Picture Processing and Digital Filtering. 2nd ed., ed. by T.S. Huang. Topics in Applied Physics. Vol. 6, pp. 177—248. Springer, Berlin, Heidelberg, New York. DOI: https://doi.org/10.1007/3-540-09339-7_19

14. Kosarev, E. L., 2008. Methods of experimental data processing. Moscow: Fiz-Matlit Publ. (in Russian).



Keywords


image processing; image registration with shifting; multielement sensors; spectral analysis; least squares method; subpixel resolution

Full Text:

PDF


Creative Commons License
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)