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Method of multilayer object sectioning based on a light scattering model
S.D. Bazhitov 1, A.V. Larichev 2,3, A.V. Razgulin 1, T.E. Romanenko 1

1 Lomonosov Moscow State University, the Faculty of Computational Mathematics and Cybernetics,
119991, Moscow, Russia, Leninskie Gory 1/52;
2 Lomonosov Moscow State University, the Faculty of Physics,
119991, Moscow, Russia, Leninskie Gory 1/2;
3 IPLIT RAS - Branch of the Federal Research Center "Crystallography and Photonics" RAS,
140700, Moscow region, Shatura, Svyatoozerskaya 1

 PDF, 1412 kB

DOI: 10.18287/2412-6179-CO-1266

Pages: 751-760.

Full text of article: Russian language.

Abstract:
We discuss a problem of reconstructing (sectioning) multilayer object images in observed images obtained by focusing the imaging system on each layer and containing spurious blurry images of neighboring layers.  The blurring model used describes a physical process of incoherent light scattering in the Fresnel approximation with a priori unknown parameters of the point spread function. We propose a method of "Boundary separation" of sectioning, which combines the use of a physical blur model with modern methods of blur estimating and edge detection. The results of testing the "Boundary separation" method on the data of physical experiments with different-scale model multilayer objects are analyzed and compared with the existing methods for solving the optical sectioning problem. It is concluded that the method is most effective on multilayer objects with clearly defined boundaries, on which the method has demonstrated almost complete restoration of the desired layers.

Keywords:
sectioning, deconvolution, imaging system, convolution, blur.

Citation:
Bazhitov SD, Larichev AV, Razgulin AV, Romanenko TE. Method of multilayer object sectioning based on a light scattering model. Computer Optics 2023; 47(5): 751-760. DOI: 10.18287/2412-6179-CO-1266.

Acknowledgements:
The research input of Bazhitov S.D., Razgulin A.V. and Romanenko T.E. was supported by the Ministry of Education and Science of the Russian Federation under the program of the Moscow Center for Fundamental and Applied Mathematics (agreement 075-15-2022-284).

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