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Image Restoration and Enhancement Using Blind Estimation of Amplitude Distortion

  • MATHEMATICAL MODELS AND COMPUTATIONAL METHODS
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Abstract—

Image restoration and correction are important steps for video analysis applications. This paper considers a new approach to assessing the characteristics of amplitude distortions in an image and constructing a method for correcting them. A “blind” method for determining the distortion function is developed and an algorithm for restoring and enhancing an image distorted by an unknown nonlinear amplitude transformation is proposed. Based on the signal model, it is shown that the source of information for the analysis of an amplitude distortion should not be the entire image, but only the boundary areas between its constituent objects. The concept of the function of local contrasts is introduced and the hypothesis about the form of this function in the absence of distortions is put forward. Based on this, a method of blind estimation of amplitude distortion and an algorithm for automatic image correction are developed. The principal feature of the proposed approach is that, as a result of the transformation, the brightness ratios between the image objects are preserved. Two possible variants of applying the algorithm to color images are proposed. Qualitative experiments demonstrated the effectiveness of the proposed method. The developed algorithm can be used in conditions when any knowledge about the distortion, to which the image is subjected, is absent, and only the resulting distorted image itself can serve as the source of information.

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REFERENCES

  1. S. Huang, B. Chen, and W. Wang, IEEE Trans. Circuits Syst. Video Technol. 24, 1814–1824 (2014).

    Article  Google Scholar 

  2. S. S. Bedi and R. Khandelwal, Int. J. Adv. Res. Comput. Commun. Eng. 2, 1605–1609 (2013).

    Google Scholar 

  3. J. R. Jensen and K. Lulla, Introductory Digital Image Processing: A Remote Sensing Perspective, 4th ed. (Pearson, Hoboken, N.J., 2015).

    Google Scholar 

  4. G. P. Ellrod, Weather Forecast. 10, 606–619 (1995).

    Article  ADS  Google Scholar 

  5. Medical Image Processing: Techniques and Applications, Ed. by G. Dougherty (Springer, New York, 2011).

    Google Scholar 

  6. R. R. Paulsen and T. B. Moeslund, Introduction to Medical Image Analysis (Springer, Cham, 2020).

    Book  Google Scholar 

  7. A. Asokan, J. Anitha, M. Ciobanu, A. Gabor, A. Naaji, and D. J. Hemanth, Appl. Sci. 10, 4207 (2020).

    Article  CAS  Google Scholar 

  8. R. C. Gonzalez and R. E. Woods, Digital Image Processing (Pearson, Upper Saddle River, N. J., 2008).

    Google Scholar 

  9. W. K. Pratt, Digital Image Processing (Wiley, Los Altos, Calif., 2007).

    Book  Google Scholar 

  10. A. Rosenfeld and A. C. Kak, Digital Picture Processing (Academic, New York, 1982), Vol. 1–2.

    Google Scholar 

  11. E. L. Hall, IEEE Trans. Comput. C-23, 207–208 (1974).

    Article  Google Scholar 

  12. R. A. Hummel, Comput. Graphics Image Process. 4, 209–224 (1975).

    Article  Google Scholar 

  13. W. Frei, Comput. Graphics Image Process. 6, 286–294 (1977).

    Article  Google Scholar 

  14. F.-N. Ku, Comput. Vision, Graphics, Image Process. 26, 107–117 (1984).

    Article  Google Scholar 

  15. J. A. Stark, IEEE Trans. Image Process. 9, 889–896 (2000).

    Article  ADS  CAS  PubMed  Google Scholar 

  16. W. Cho, S. Seo, J. You, and S. Kang, J. Comput. Commun., No. 2, 52–56 (2014).

  17. H. D. Cheng and X. J. Shi, Digital Signal Process. 14, 158–170 (2004).

    Article  Google Scholar 

  18. R. I. Litvan, Yu. I. Aver’yanov, and F. S. Bykovskaya, Tekh. Kino Televid., No. 2, 38–41 (1979).

  19. R. A. Hummel, Comput. Graphics Image Process. 6, 184–195 (1977).

    Article  Google Scholar 

  20. S. Vedavathi, R. Deepu, H. S. Aravind, and V. Rakesh, Int. J. Sci., Eng. Technol. Res. 3, 674–676 (2014).

    Google Scholar 

  21. R. Garg, B. Mittal, and S. Garg, Int. J. Electron. Commun. Technol. 2, 107–111 (2011).

    Google Scholar 

  22. A. Rani and R. Kaur, Int. J. Adv. Res. Comput. Sci. Software Eng. 5, 603–606 (2015).

    Google Scholar 

  23. P. A. Chochia, Image Encoding and Processing (Nauka, Moscow, 1988), pp. 98–112 [in Russian].

    Google Scholar 

  24. P. A. Chochia, Methods of Video Information Processing Based on the Two-Scale Image Model (Lambert Academic, Saarbrucken, 2017) [in Russian].

    Google Scholar 

  25. T. S. Cho, C. L. Zitnick, N. Joshi, et al., IEEE Trans. Pattern Anal. Mach. Intell. 34, 683–694 (2012).

    Article  PubMed  Google Scholar 

  26. Y. Gong and I. F. Sbalzarini, in Proc. Asian Conf. on Computer Vision 2014: Computer Vision Workshops, Singapore, Nov. 1–5, 2014 (Springer, Cham, 2015), Part II, in Ser.: Lecture Notes in Computer Science, Vol. 9009, pp. 47–62 (2015).

  27. Q. Mu, X. Wang, Y. Wei, and Z. Li, Comput. Visual Media 7, 529–546 (2021).

    Article  Google Scholar 

  28. A. R. Rivera, B. Ryu, and O. Chae, IEEE Trans. Image Process. 21, 3967–3980 (2012).

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  29. M. Jmal, W. Souidene, and R. Attia, J. Electron. Imaging 26, 011020 (2017).

    Article  ADS  Google Scholar 

  30. Z. Shi, M. Zhu, B. Guo, et al., EURASIP J. Image Video Process., No. 13 (2018).

  31. O. Oktay, E. Ferrante, K. Kamnitsas, et al., IEEE Trans. Med. Imaging 37, 384–395 (2018).

    Article  PubMed  Google Scholar 

  32. A. M. Nickfarjam and H. Ebrahimpour-Komleh, Appl. Intell. 47, 1132–1143 (2017).

    Article  Google Scholar 

  33. H. Lu, Y. Li, T. Uemura, et al., Future Gener. Comput. Syst. 82, 142–148 (2018).

    Article  Google Scholar 

  34. E. H. Land and J. J. McCann, J. Opt. Soc. Am. 61, 1–11 (1971).

    Article  ADS  CAS  PubMed  Google Scholar 

  35. E. H. Land, Sci. Am. 237, 108–128 (1977).

    Article  CAS  PubMed  Google Scholar 

  36. P. A. Chochia, Image Encoding and Processing (Nauka, Moscow, 1988), pp. 69–87 [in Russian].

    Google Scholar 

  37. R. M. Haralick and L. Watson, Comput. Graphics Image Process. 15, 113–129 (1981).

    Article  Google Scholar 

  38. G. Papari and N. Petkov, Image Vision Comput. 29, 79–103 (2011).

    Article  Google Scholar 

  39. D. Marr and E. Hildreth, Proc. R. Soc. London B B207, 187–217 (1980).

    ADS  Google Scholar 

  40. D. Marr, A Computational Investigation into the Human Representation and Processing of Visual Information (MIT Press, New York, 2010).

    Google Scholar 

  41. J. J. Clark, IEEE Trans. Pattern Anal. Mach. Intell. 12, 830–831 (1989).

    Google Scholar 

  42. P. Smith, T. Drummond, and R. Cipolla, in Proc. 10th British Machine Vision Conf., Nottingham, Sept. 13–16, 1999 (British Machine Vision Association, 1999), Vol. 2, pp. 369–378.

  43. J. A. Canny, IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986).

    Article  CAS  PubMed  Google Scholar 

  44. K. N. Plataniotis and A. N. Venetsanopoulos, Color Image Processing and Applications (Springer, Berlin, 2000).

    Book  Google Scholar 

Download references

Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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Authors and Affiliations

  1. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, 127051, Moscow, Russia

    P. A. Chochia

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  1. P. A. Chochia
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Correspondence to P. A. Chochia.

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Translated by Yu. Ryzhkov

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Chochia, P.A. Image Restoration and Enhancement Using Blind Estimation of Amplitude Distortion. J. Commun. Technol. Electron. 68 (Suppl 2), S263–S273 (2023). https://doi.org/10.1134/S1064226923140061

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  • DOI: https://doi.org/10.1134/S1064226923140061

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