Determining image-distortion parameters by spectral means when processing pictures of the earth’s surface obtained from satellites and aircraft

Sizikov, V.S., Stepanov, A.V., Mezhenin, A.V., Burlov, D.I., Ekzemplyarov, R.A.

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For Russian citation (Opticheskii Zhurnal):

Сизиков В.С., Степанов А.В., Меженин А.В., Бурлов Д.И., Экземпляров Р.А. Определение параметров искажений изображений спектральным способом в задаче обработки снимков поверхности Земли, полученных со спутников и самолётов // Оптический журнал. 2018. Т. 85. № 4. С. 19–27. http://doi.org/10.17586/1023-5086-2018-85-04-19-27

Sizikov V.S., Stepanov A.V., Mezhenin A.V., Burlov D.I., Ekzemplyarov R.A. Determining image-distortion parameters by spectral means when processing pictures of the earth’s surface obtained from satellites and aircraft [in Russian] // Opticheskii Zhurnal. 2018. V. 85. № 4. P. 19–27. http://doi.org/10.17586/1023-5086-2018-85-04-19-27

For citation (Journal of Optical Technology):

V. S. Sizikov, A. V. Stepanov, A. V. Mezhenin, D. I. Burlov, and R. A. Éksemplyarov, "Determining image-distortion parameters by spectral means when processing pictures of the earth’s surface obtained from satellites and aircraft," Journal of Optical Technology. 85(4), 203-210 (2018). https://doi.org/10.1364/JOT.85.000203

Abstract:

This paper solves the problem of eliminating smearing, defocusing, and noise of aerospace images (pictures) of the earth’s surface obtained from remote probing. The type of distortion (smearing or defocusing) is determined by a modified spectral method (with the values of the distortion parameters determined from original derived formulas). The accuracy of the image reconstruction is enhanced by solving integral equations (an ill-posed problem) to determine the type of distortion and to estimate the distortion parameters. A new noise model—multipolar pulse noise—which is more adequate than bipolar pulse noise is proposed, along with a filter for filtering it out. It is shown that the image-reconstruction error can depend on the sequence in which the smear/defocusing and noise are eliminated. Results are presented for processing distorted pictures of a certain section of the earth’s surface.

Keywords:

Earth surface image distortion, smearing, defocusing, noise, distortion elimination, point-spread function, spectral method for determining image distortions parameters, multipolar pulse noise, sequence of operations, MatLab

OCIS codes: 100.0100

References:

1. R. C. Gonzalez and R. E. Woods, Digital Image Processing (Prentice-Hall, Upper Saddle River, NJ, 2002; Tekhnosfera, Moscow, 2006).
2. V. S. Sizikov, “Estimating the point-spread function from the spectrum of a distorted tomographic image,” J. Opt. Technol. 82(10), 655–658 (2015) [Opt. Zh. 82(10), 13–17 (2015)].
3. V. S. Sizikov, “Spectral method for estimating the point-spread function in the task of eliminating image distortions,” J. Opt. Technol. 84(2), 95–101 (2017) [Opt. Zh. 84(2), 36–44 (2017)].
4. V. S. Sizikov, Direct and Inverse Image-Reconstruction, Spectroscopy, and Tomography Problems with MatLab (Lan’, St. Petersburg, 2017).
5. G. I. Vasilenko and A. M. Taratorin, Image Reconstruction (Radio i Svyaz’, Moscow, 1986).
6. R. H. Bates and M. J. McDonnell, Image Restoration and Reconstruction (Oxford U. Press, 1989; Mir, Moscow, 1989).
7. I. S. Gruzman, V. S. Kirichuk, V. P. Kosykh, G. I. Peretyagin, and A. A. Spektor, Digital Image Processing in Information Systems (Izd. NGTU, Novosibirsk, 2002).
8. V. S. Sizikov, Inverse Applied Problems and MatLab (Lan’, St. Petersburg, 2011).
9. V. S. Sizikov and R. A. Ékzemplyarov, “Operating sequence when noise is being filtered on distorted images,” J. Opt. Technol. 80(1), 28–34 (2013) [Opt. Zh. 80(1), 39–48 (2013)].
10. 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(2), 91–95 (2006) [Opt. Zh. 73(2), 26–30 (2006)].
11. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB (Prentice Hall, Upper Saddle River, NJ, 2004; Tekhnosfera, Moscow, 2006).
12. L. Yan, H. Lin, S. Zhong, and H. Fang, “Semi-blind spectral deconvolution with adaptive Tikhonov regularization,” Appl. Spectrosc. 66(11), 1334–1346 (2012).
13. A. N. Tikhonov, A. V. Goncharski, and V. V. Stepanov, “Inverse photographic-image-processing problems,” in Ill-Posed Natural-Science Problems, A. N. Tikhonov and A. V. Goncharski, eds. (Izd. MGU, Moscow, 1987), pp. 185–195.
14. P. C. Hansen, Discrete Inverse Problems: Insight and Algorithms (SIAM, Philadelphia, 2010).
15. A. S. Leonov, Solving Ill-Posed Inverse Problems: A Sketch of the Theory, Practical Algorithms and Demonstrations in MATLAB (Knizhny Dom LIBROKOM, Moscow, 2010).
16. K. T. Protasov, V. V. Belov, and N. V. Molchunov, “Image reconstruction with pre-estimation of the point-spread function,” Opt. Atmos. Okeana 13(2), 139–145 (2000).
17. Yu. E. Voskobonikov, “Combined nonlinear algorithm for reconstructing contrasty images with an inaccurately specified spread function,” Avtometriya 43(6), 3–16 (2007).
18. T. V. Antonova, “Methods of identifying the parameter in the kernel of a convolution-type first-degree equation on the class of functions with discontinuities,” Sibirsk. Zh. Vychislit. Matem. 18(2), 107–120 (2015).
19. D. N. Sidorov, Methods of Analyzing Integrated Dynamic Models: Theory and Applications (Izd. IGU, Irkutsk, 2013).
20. D. Sidorov, Integral Dynamical Models: Singularities, Signals and Control (World Scientific Publishing, Singapore, 2014).
21. V. D’yakonov and I. Abramenkova, MATLAB: Signal and Image Processing (Piter, St. Petersburg, 2002).
22. V. S. Sizikov and A. V. Stepanov, “Method of training examples in solving inverse ill-posed problems of spectroscopy,” Sci. Tech. J. Inf. Technol., Mech. Opt. 15(6), 1147–1154 (2015).
23. A. S. Leonov and A. G. Yagola, “Adaptive optimal algorithms for solving ill-posed problems with representable source functions,” Zh. Vychisl. Matem. Matem. Fiz. 41(6), 855–873 (2001).
24. J. S. Lim, Two-Dimensional Signal and Image Processing (Prentice Hall PTR, New Jersey, 1990).
25. B. Jähne, Digital Image Processing (Springer, Berlin, 2005; Tekhnosfera, Moscow, 2007).
26. D. I. Burlov, A. V. Mezhenin, O. F. Nemolochnov, and V. I. Polyakov, “Automation of the choice of a method of compressing digital video in intelligent systems of railroad transport,” Vest. RGUPS 1(53), 35–40 (2014).