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ISSN: 1023-5086

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ISSN: 1023-5086

Scientific and technical

Opticheskii Zhurnal

A full-text English translation of the journal is published by Optica Publishing Group under the title “Journal of Optical Technology”

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DOI: 10.17586/1023-5086-2020-87-02-56-63

УДК: 621.397.3

Restoration of nonuniformly smeared images

For Russian citation (Opticheskii Zhurnal):

Сизиков В.С., Довгань А.Н., Цепелева А.Д. Восстановление изображений, смазанных неравномерно // Оптический журнал. 2020. Т. 87. № 2. С. 56–63. http://doi.org/10.17586/1023-5086-2020-87-02-56-63

 

Sizikov V.S., Dovgan A.N., Tsepeleva A.D. Restoration of nonuniformly smeared images [in Russian] // Opticheskii Zhurnal. 2020. V. 87. № 2. P. 56–63. http://doi.org/10.17586/1023-5086-2020-87-02-56-63

For citation (Journal of Optical Technology):

V. S. Sizikov, A. N. Dovgan, and A. D. Tsepeleva, "Restoration of nonuniformly smeared images," Journal of Optical Technology. 87(2), 110-116 (2020). https://doi.org/10.1364/JOT.87.000110

Abstract:

This paper considers the problem of mathematically eliminating a nonuniform rectilinear smear in an image, for example, a picture taken by a motionless camera of runners on a track, running at different speeds. The problem is described by a set of one-dimensional integral equations of a general type (not a convolution type) with a two-dimensional point scattering function or one two-dimensional integral equation with a four-dimensional point scattering function. The integral equations are solved by Tikhonov regularization and quadrature/cubature. It is shown that, in the case of a nonuniform smear, the use of a set of one-dimensional integral equations is preferable to one two-dimensional integral equation. In the direct problem, the image smear is supplemented by truncating it, evading the boundary conditions, and diffusing its edges to suppress the Gibbs effect in the inverse problem. Cases of piecewise uniform and continuously (linearly) nonuniform smear are considered. Illustrative results are presented.

Keywords:

smeared image, uniform and nonuniform smear, integral equations, Tikhonov regularization method, truncation and diffusion of edges, piecewise continuously nonuniform smears, MatLab

Acknowledgements:

The research was supported by Megafaculty of Computer Technology and Management of ITMO University (617026, 619296).

OCIS codes: 100.0100

References:

1. M. V. Arefyeva and A. F. Sysoev, “Fast regularizing algorithms for digital image restoration,” Vychisl. Metody Program. 39, 40–55 (1983).
2. A. N. Tikhonov, A. V. Goncharsky, and V. V. Stepanov, “Inverse problems of photoprocessing,” in Incorrect Problems of Natural Science, A. N. Tikhonova and A. V. Goncharsky, eds. (Moscow State University, 1987), pp. 185–195.
3. I. S. Gruzman, V. S. Kirichuk, V. P. Kosykh, G. I. Peretyagin, and A. A. Spector, Digital Image Processing in Information Systems (Novosibirsk State Technical University, 2002).
4. R. Gonzalez and R. Woods, Digital Image Processing (Tekhnosfera, Moscow, 2006).
5. J. Huang, M. Donatelli, and R. Chan, “Nonstationary iterated thresholding algorithms for image deblurring,” Inverse Probl. Imaging 7(3), 717–736 (2013).
6. V. S. Sizikov, Direct and Inverse Problems of Image Restoration, Spectroscopy and Tomography with the Help of MATLAB (Lany, St. Petersburg, 2017).
7. A. Matakos, S. Ramani, and J. A. Fessler, “Accelerated edge-preserving image restoration without boundary artefacts,” IEEE Trans. Image Process. 22(5) 2019–2029 (2013).
8. Y. Xu, T.-Z. Huang, J. Liu, and X.-G. Lv, “Split Bregman iteration algorithm for image deblurring using fourth-order total bounded variation regularization model,” J. Appl. Math. 2013, 238561 (2013).
9. H. Chang, X.-C. Tai, L.-L. Wang, and D. Yang, “Convergence rate of overlapping domain decomposition methods for the Rudin–Osher–Fatemi model based on a dual formulation,” SIAM J. Imaging Sci. 8(1) 564–591 (2015).
10. L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D. 60(1–4), 259–268 (1992).
11. S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Trans. Image Process. 13(10) 1327–1344 (2004).
12. A. N. Tikhonov and V. Y. Arsenin, Methods for Solving Incorrect Problems (Nauka, Moscow, 1986).
13. L. M. Bragman, “The relaxation method for finding the common point of convex sets and its application for solving convex programming problems,” Zh. Vychisl. Mat. Mat. Fiz. 7(3) 620–631 (1967).
14. R. Gonzalez, R. Woods, and S. Eddins, Digital Image Processing in MATLAB (Tekhnosfera, Moscow, 2006).
15. R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman, “Removing camera shake from a single photograph,” ACM Trans. Graphics 25(3) 787–794 (2006).
16. V. S. Yuzhikov, “Blind deconvolution: automatic restoration of smeared images,” https://habr.com/ru/post/175717/.
17. V. S. Sizikov and I. A. Belov, “Reconstruction of smeared and out-of-focus images by regularization,” J. Opt. Technol. 67(4), 351–354 (2000) [Opt. Zh. 67(4), 60–63 (2000)].
18. 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)].
19. V. S. Sizikov, A. V. Stepanov, A. V. Mezhenin, D. I. Burlov, and R. A. Ékzemplyarov, “Determining image-distortion parameters by spectral means when processing pictures of the earth’s surface obtained from satellites and aircraft,” J. Opt. Technol. 85(4), 203–210 (2018) [Opt. Zh. 85(4), 19–27 (2018)].
20. A. V. Bakushinsky and A. V. Goncharsky, Incorrect Problems: Numerical Methods and Applications (Moscow State University, 1989).
21. V. Sizikov and A. Dovgan, “Reconstruction of images smeared uniformly and non-uniformly,” in CEUR Workshop Proceedings (2019), paper 2.
22. R. Bates and M. McDonnell, Restoration and Reconstruction of Images (Mir, Moscow, 1989).
23. H. Engl, M. Hanke, and A. Neubauer, Regularization of Inverse Problems (Kluwer, Dordrecht, 1996).
24. A. F. Verlan and V. S. Sizikov, Integral Equations: Methods, Algorithms, and Programs (Nauk Dumka, Kiev, 1986).
25. A. V. Gorshkov, “Improving the resolution of images when processing data from a physical experiment and finding an unknown hardware function using the software package REIMAGE,” Prib. Tekh. Eksp. (2), 68–78 (1995).
26. V. S. Sizikov, “The truncation–blurring–rotation technique for reconstructing distorted images,” J. Opt. Technol. 78(5), 298–304 (2011) [Opt. Zh. 78(5), 18–26 (2011)].