Recovery of Images Distorted by an Instrument Function with Unknown Side Lobes

  • V. A. Cherepenin
  • A. V. Kokoshkin
  • V. A. Korotkov
  • K. V. Korotkov
  • E. P. Novichikhin
Theory and Methods of Signal Processing


A method for compensating the influence of unknown side lobes of a distorting instrument function on the quality of image recovery is proposed. A processing algorithm, based on the knowledge of the main lobe of the instrument function and its spectrum together with a universal reference spectrum, is used to compensate the contribution of the unknown side lobes to the spectrum of the distorted image. The stability of the method to the noise inherent in the improved images and various forms of distorting instrument functions is analyzed. High efficiency of the method of compensation is demonstrated in the cases in which the total contribution from the unknown side lobes to the resulting brightness of the distorted image significantly (four-fold) exceeds the contribution from the main lobe.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    R. Gonzalez and R. Woods, Digital Image Processing (Prentice Hall, Upper Saddle River, New Jersey, 2002; Tekhnosfera, Moscow, 2005).Google Scholar
  2. 2.
    Yu. A. Pirogov and A. L. Timanovskii, Vestn. Mosk. Univ., Ser. 3: Fiz., Astron., No. 1, 45 (2006).Google Scholar
  3. 3.
    A. L. Timanovskii, “Superresolution in passive radiovision systems,” Cand. Sci. (Phys.) Dissertation, Mosk. Gos. Univ. (Mosk. Gos. Univ., Moscow, 2007).Google Scholar
  4. 4.
    A. V. Kokoshkin, V. A. Korotkov, K. V. Korotkov, and E. P. Novichikhin, J. Radioelektron., No. 4 (2015). Scholar
  5. 5.
    A. Yu. Denisova and V. V. Sergeev, Komp’yut. Opt. 39, 557 (2015).Google Scholar
  6. 6.
    A. Yu. Denisova, Yu. N. Zhuravel’, and V. V. Myasnikov, Komp’yut. Opt. 40, 380 (2016).Google Scholar
  7. 7.
    V. A. Fursov, Komp’yut. Opt. 40, 878 (2016).Google Scholar
  8. 8.
    S. M. Rytov, Yu. A. Kravtsov, and V. I. Tatarskii, Introduction to Statistical Radio Physics. Part 2: Random Fields, (Nauka, Moscow, 1978) [in Russian].zbMATHGoogle Scholar
  9. 9.
    Yu. V. Gulyaev, A. Yu. Zrazhevskii, A. V. Kokoshkin, et al., J. Radioelektron., No. 12 (2013). Scholar
  10. 10.
    I. Avcibas, B. Sankur, and K. Sayood, J. Electron. Imag. 11, 206 (2002).CrossRefGoogle Scholar
  11. 11.
    W. C. Wilder, Subjective Relevant Error. Criteria for Pictorial Data Processing. Report TR-EE 72-34 (Purdue Univ., West Lafayette, 1972).Google Scholar
  12. 12.
    I. M. Zhuravel’, Short Course of the Theory of Image Processing. book2/index.php.Google Scholar
  13. 13.
    Yu. I. Monich and V. V. Starovoitov, Iskusstv. Intellekt., No. 4, 376 (2008).Google Scholar
  14. 14.
    A. V. Kokoshkin, V. A. Korotkov, K. V. Korotkov, and E. P. Novichikhin, J. Radioelektron., No. 6 (2015). Scholar
  15. 15.
    A. Yu. Zrazhevskii and A. V. Kokoshkin, J. Radioelektron., No. 4 (2013). apr13/8/text.html.Google Scholar
  16. 16.
    A. Yu. Zrazhevskii, A. V. Kokoshkin, and V. A. Korotkov, J. Radioelektron., No. 11 (2013). http://jre. Scholar

Copyright information

© Pleiades Publishing, Inc. 2018

Authors and Affiliations

  • V. A. Cherepenin
    • 1
  • A. V. Kokoshkin
    • 2
  • V. A. Korotkov
    • 2
  • K. V. Korotkov
    • 2
  • E. P. Novichikhin
    • 2
  1. 1.Institute of Radio Engineering and ElectronicsRussian Academy of SciencesMoscowRussia
  2. 2.Institute of Radio Engineering and Electronics (Fryazino Branch)Russian Academy of SciencesFryazino, Moscow oblastRussia

Personalised recommendations