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vol 67 / April, 2024
Article

DOI 10.17586/0021-3454-2023-66-7-559-567

UDC 621.397.3: 519.642.3

NEW SUSTAINABLE METHODS FOR DISTORTED IMAGE RECOVERING

V. S. Sizikov
ITMO University, Saint Petersburg, 197101, Russian Federation; Professor


N. G. Rushchenko
ITMO University, Faculty of Software Engineering and Computer Technique ; Associate Professor


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Reference for citation: Sizikov V. S., Rushchenko N. G. New sustainable methods for distorted image recovering. Journal of Instrument Engineering. 2023. Vol. 66, N 7. P. 559—567 (in Russian). DOI: 10.17586/0021-3454-2023-66-7-559-567.

Abstract. New sustainable methods and algorithms are proposed for recovering object images damaged (distorted, corrupted) as a result of defocusing, spreading, and noising. The type and parameters of damage are estimated by the developed “spectral method”, as demonstrated on the example of distorted images of the Black Sea, and then the image damage is eliminated (removed) based on a stable solution of integral equations using the Tikhonov regularization method and the Fourier transform. The approach makes it possible to increase the resolution of optical instruments - cameras, telescopes, microscopes, etc.ff
Keywords: defocusing, spreading, noising, integral equations, “spectral method” for damage type and parameters estimating, elimination (removal), resection (damage), MatLab

References:
  1. Gonzalez R.C., Woods R.E. Digital Image Processing, New Jersey, Prentice Hall, 2002, 793 p.
  2. Sizikov V.S. J. Optical Technology, 2017, no. 2(84), pp. 95–101, DOI: 10.1364/JOT.84.000095.
  3. Sizikov V. et al. Computers, 2020, no. 30(9), pp. 1–16, DOI: 10.3390/computers9020030.
  4. Voskoboinikov Yu.E. and Litasov V.A. Avtometriya (Optoel. Instrum. Data Proces.), 2006, no. 6(42), pp. 3–15.
  5. Boikov I.B., Kravchenko M.V., Kryuchko V.I. Izvestiya RAS, Physics of the Solid Earth, 2010, no. 4(16), pp. 339–349.
  6. Gruzman I.S., Kirichuk V.S., Kosykh V.P., Peretyagin G.I., Spektor A.A. Tsifrovaya obrabotka izobrazheniy v informatsionnykh sistemakh (Digital Image Processing in Information Systems), Novosibirsk, 2002, 352 р. (in Russ.)
  7. Sizikov V.S. Pryamyye i obratnyye zadachi vosstanovleniya izobrazheniy, spektroskopii i tomografii s MatLab (Direct and Inverse Problems of Image Reconstruction, Spectroscopy and Tomography with MatLab), St. Petersburg, 2017, 412 р. (in Russ.)
  8. Sizikov V.S., Dovgan A.N., Tsepeleva A.D. J. Optical Technology, 2020, no. 2(87), pp. 110–116, DOI: 10.1364/JOT.87.000110.
  9. Sizikov V., Loseva P., Medvedev E., Sharifullin D., Dovgan A., Rushchenko N. CEUR Workshop Proc., 2020, no. 11(2893).
  10. Gonsales R.C., Woods R.E., Eddins S.L. Digital Image Processing using MATLAB, New Jersey, Prentice Hall, 2004, 609 p.
  11. Hansen P.C. Discrete Inverse Problems: Insight and Algorithms, Philadelphia, SIAM, 2010, 213 p.
  12. Fergus R. et al. ACM Trans. Graphics, 2006, no. 3(25), pp. 787–794.
  13. Cho S., Lee S. ACM Trans. Graphics, 2009, no. 5(28), art. no. 145, DOI: 10.1145/1618452.1618491.
  14. Sizikov V.S. Obratnyye prikladnyye zadachi i MatLab (Inverse Applications and MatLab), St. Petersburg, 2011, 256 р. (in Russ.)
  15. Petrov Yu.P., Sizikov V.S. Well-Posed, Ill-Posed, and Intermediate Problems with Applications, Leiden–Boston, VSP, 2005, 234 pp.
  16. Kabanikhin S.I. Inverse and Ill-posed Problems: Theory and Applications, Berlin, Walter de Gruyter, 2011, 459 p.
  17. Pronina V.S. Vosstanovleniye izobrazheniy s pomoshch'yu obuchayemykh optimizatsionno-neyrosetevykh algoritmov (Image Restoration Using Trainable Optimization Neural Network Algorithms), Extended abstract of candidate’s thesis, Moscow, 2023, 36 р. (in Russ.)
  18. Sidorov D. Integral Dynamical Models: Singularities, Signals and Control, Singapore–London, World Sci. Publ., 2014, 343 p.
  19. Protasov K.T., Belov V.V., Molchunov N.V. Optics Atmos. Ocean., 2000, no. 2(13), pp. 139–145.
  20. Voskoboinikov Yu.E. Optoel. Instrum. Data Proces., 2007, no. 6(43), pp. 489–499, DOI: 10.3103/S8756699007060015.
  21. Antonova T.V. Siberian J. Numer. Mathem., 2015, no. 2(18), pp. 107–120, DOI: 10.15372/SJNM20150201.
  22. Solodusha S.V. Metody postroyeniya integral'nykh modeley dinamicheskikh sistem: algoritmy i prilozheniya v energetike (Methods for Constructing Integral Models of Dynamic Systems: Algorithms and Applications in the Power Industry), Extended abstract of Doctor’s thesis, Irkutsk, 2019, 44 р. (in Russ.)
  23. Egoshkin N.A., Eremeev V.V. Digital signal processing, 2010, no. 4, pp. 28–32. (in Russ.)
  24. Sizikov V.S. Intern. J. Artific. Intelligence, 2015, no. 1(13), pp. 184–199.
  25. Sizikov V.S., Dovgan' A.N., Lavrov A.V. Ustoychivyye metody matematiko-komp'yuternoy obrabotki izobrazheniy i spektry (Stable Methods of Mathematical-Computer Processing of Images and Spectra), St. Petersburg, 2022, 70 р. (in Russ.)