УДК: 621.397.3

Estimating the point-spread function from the spectrum of a distorted tomographic image

Publication in Journal of Optical Technology

For Russian citation (Opticheskii Zhurnal):

Сизиков В.С. Оценка функции рассеяния точки по спектру искаженного томографического изображения // Оптический журнал. 2015. Т. 82. № 10. С. 13–17.

Sizikov V.S. Estimating the point-spread function from the spectrum of a distorted tomographic image [in Russian] // Opticheskii Zhurnal. 2015. V. 82. № 10. P. 13–17.

For citation (Journal of Optical Technology):

V. S. Sizikov, "Estimating the point-spread function from the spectrum of a distorted tomographic image," Journal of Optical Technology. 82(10), 655-658 (2015). https://doi.org/10.1364/JOT.82.000655

Abstract:

A new method is proposed for estimating the parameters of the point-spread function from the Fourier spectrum of a distorted image. When there is blurring of an image, its spectrum is compressed in the direction of the blurring, and this makes it possible to estimate the direction and magnitude Δ of the blurring. In the case of defocusing, the spectrum is also deformed proportionally to the defocusing radius ρ, and this makes it possible to estimate ρ. Numerical examples are given of the application of this technique to a tomographic image.

Keywords:

image distortions (blurring, defocusing), distortions parameters, Fourier spectrum of image

Acknowledgements:

This work was carried out with the support of the Russian Foundation for Basic Research (Grant No. 13-08-00442).

OCIS codes: 100.0100

References:

1. V. S. Sizikov, Reverse Applied Problems and MatLab (Lan’, St. Petersburg, 2011).
2. R. C. Gonzalez and R. E. Woods, Digital Image Processing (Prentice Hall, Upper Saddle River, N.J., 2002; Tekhnosfera, Moscow, 2005).
3. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing using MATLAB (Prentice Hall, Upper Saddle River, N. J., 2004; Tekhnosfera, Moscow, 2006).
4. V. S. Sizikov and R. A. Ékzemplyarov, “Operating sequence when noise is being filtered on distorted images,” J. Opt. Technol. 80, 28 (2013) [Opt. Zh. 80, No. 1, 39 (2013)].
5. V. N. Ostrikov and O. V. Plakhotnikov, “Identifying the point-spread function of an observation channel from a calibrating image by the method of least squares,” J. Opt. Technol. 73, 91 (2006) [Opt. Zh. 73, No. 2, 26 (2006)].
6. V. D’yakonov and I. Abramenkova, MATLAB. Signal and Image Processing (Piter, St. Petersburg, 2002).
7. V. A. Ivanov, M. Ya. Marusina, and V. S. Sizikov, “Processing measurement information under conditions of indeterminacy,” Kontrol’. Diagnostika. No. 4, 40 (2001).
8. V. A. Ivanov, M. Ya. Marusina, and A. V. Flegontov, “Invariant approximations and their use in MR tomography,” Nauchn. Prib. 13, No. 2, 22 (2003).
9. M. Ya. Marusina, “Correcting the inhomogeneity of the main magnetic field of MR tomography based on permanent magnets,” Dissertation for the defense of the degree of candidate of technical sciences, St. Petersburg, ITMO, 1993.