Skip to main content
SpringerLink
Log in

Contour-Constrained Image Smoothing Preserving Its Structure

  • THEORY AND METHODS OF INFORMATION PROCESSING
  • Published:
Journal of Communications Technology and Electronics Aims and scope Submit manuscript

Abstract

Structure-preserving image smoothing is used in solving many problems of video information processing and analysis. Most smoothing methods are based on indirect information about the characteristics of a local image area, for example, the distribution of signal values, rather than on direct data of the image spatial structure. In reality, a criterion for constraining the smoothing area should not be the distribution of signal values or the difference in brightness of the objective and surrounding points, but their belonging to the same spatial area of an image. A criterion sufficient for the connectivity of points in an area is the absence of contour lines between them. An approach to image smoothing is proposed, which is based on preliminary detection of contour edges between image areas and subsequent contour-constrained smoothing inside each area. A concept of “affinity” of image points is introduced, on the basis of which the smoothing algorithm is built. The proposed algorithm is experimentally compared with other available smoothing algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

Similar content being viewed by others

REFERENCES

  1. S. Nishikawa, Massa, and J. C. Mott-Smith, “Area properties of television pictures,” IEEE Trans. Inf. Theory 11 (3), 348–352 (1965).

    Article  Google Scholar 

  2. L. Karacan, E. Erdemy, and A. Erdem, “Structure-preserving image smoothing via region covariances,” ACM Trans. Graph. (TOG) 32 (6), 176:1–176:11 (2013).

  3. M. Al-Nasrawi, G. Deng, and B. Thai, “Edge-aware smoothing through adaptive interpolation,” Signal, Image and Video Process. 12, 347–354 (2018).

    Article  Google Scholar 

  4. J. Jeon, H. Lee, H. Kang, and S. Lee, “Scale-aware structure-preserving texture filtering,” Comp. Graph. Forum, Eurographs Ass. & John Wiley, Chichester: GBR 35 (7), 77–86 (2016).

  5. L. Xu, Q. Yan, Y. Xia, and J. Jia, “Structure extraction from texture via relative total variation,” ACM Trans. Graph. (TOG) 31 (6), 139:1–139:10 (2012).

  6. E.S.L. Gastal and M.M. Oliveira, “Domain transform for edge-aware image and video processing,” Proc. SIGGRAPH 2011, ACM Trans. Graph., 30 (4), 69:1–69:12 (2011).

  7. P. A. Chochia, “Two-scale image model,” in Image Coding and Processing (Nauka, Moscow, 1988), pp. 69–87 [in Russian].

  8. J.-S. Lee, “Digital image smoothing and the sigma filter,” Comp. Vis., Graph., Image Process. 24 (2), 255–269 (1983).

  9. G. A. Mastin, “Adaptive filters for digital image noise smoothing: an evaluation,” Comp. Vis., Graph., Image Process. 31 (1), 103–121 (1985).

  10. P. A. Chochia, “Image enhancement using sliding histograms,” Comp. Vis., Graph., Image Process. 44 (2), 211–229 (1988).

  11. P. A. Chochia, “Image smoothing under preservation of contours,” in Image Coding and Processing (Nauka, Moscow, 1988), pp. 87–98 [in Russian].

  12. P. A. Chochia, Methods for Processing of Video Information on the Basis of a Two-Scale Image Model (LAP Lambert Academic Publ., Saarbrucken, 2017) [in Russian].

    Google Scholar 

  13. C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proc. IEEE 6th Int. Conf. on Computer Vision, Bombay, India, Jan. 4–7, 1998 (IEEE, New York, 1998), pp. 839–846.

  14. F. Durand and J. Dorsey, “Fast bilateral filtering for the display of high-dynamic-range images,” ACM Trans. Graph. 21 (3), 257–266 (2002).

  15. S. Paris and F. Durand, “A fast approximation of the bilateral filter using a signal processing approach,” in Computer Vision. Lecture Notes in Computer Science (Springer-Verlag, Berlin–Heidelberg, 2006), Vol. 3954, pp. 568–580.

  16. C. Lantuéjoul and F. Maisonneuve, “Geodesic methods in quantitative image analysis,” Pattern Recogn. 17 (2), 177–187 (1984).

  17. A. Criminisi, T. Sharp, and A. Blake, “Geos: Geodesic image segmentation,” in Proc. Eur. Conf. on Computer Vision – ECCV, Marseille, France, Oct., 2008 (Springer-Verlag, 2008), pp. 99–112.

  18. J. Grazzini and V. Soille, “Image filtering based on locally estimated geodesic functions,” VISIGRAPP 2008, CCIS, vol. 24, 2009 (Springer-Verlag, Berlin, 2008), pp. 123–134.

  19. M. Mozerov and J. Van de Weijer, “Improved recursive geodesic distance computation for edge preserving filter,” IEEE Trans. Image Process. 26 (8), 3696–3706 (2017).

    Article  MathSciNet  Google Scholar 

  20. D. Comaniciu and P. Meer, “Mean shift analysis and applications,” in Proc. 7th IEEE Int. Conf. on Computer Vision, Kerkyra, Greece, 1999 (IEEE, New York, 1999), vol. 2, pp. 1197–1203.

  21. S. Paris, “Edge-preserving smoothing and mean-shift segmentation of video streams,” Computer Vision – ECCV (2008) in Lecture Notes in Computer Science (Springer-Verlag, Berlin, 2008), Vol. 5303, pp. 460–473.

  22. E. Parzen, “On Estimation of a Probability Density Function and Model,” Ann. Math. Stat. 33, 1065–1076 (1962).

    Article  Google Scholar 

  23. R. Abiko and M. Ikehara, “Fast edge preserving 2D smoothing filter using indicator function,” IEICE Trans. on Inf. & Syst. E102D (10), 2025–2032 (2019).

  24. R. C. Gonzalez and R. E. Woods, Digital Image Processing (Prentice Hall, Upper Saddle River, New Jersey, 2008; Tekhnosfera, Moscow, 2012).

  25. X.-Y. Gong, H. Su, D.Xu, Z.-T. Zhang, F. Shen, and H.B Yang, “An Overview of Contour Detection Approaches,” Int. J. Automation & Comput. (IJAC) 15 (15), 656–672 (2018).

  26. G. Papari and N. Petkov, “Edge and line oriented contour detection: State of the art,” Image & Vis. Comput. 29, 79–103 (2011).

  27. U. Saeed and J.-L. Dugelay, “Combining edge detection and region segmentation for lip contour extraction,” in Proc. 6th Conf. Articulated Motion and Deformable Objects (AMDO), Mallorca, Spain, 2010, (AMDO, 2010), pp. 11–12.

  28. Y.-T. Hsiao, C.-L. Chuang, J.-A. Jiang, and Ch.‑Ch. Chien, “A contour based image segmentation algorithm using morphological edge detection,” in Proc. IEEE Int. Conf. on Systems, Man, Cybernetics, Hawaii, 2005 (IEEE, New York, 2005), pp. pp. 2962–2967.

  29. F. M. Abubakar, “A study of region-based and contour based image segmentation,” Signal & Image Process. Int. J. (SIPIJ) 3 (6), 15–22 (2012).

    Article  Google Scholar 

  30. A. G. Vitushkin, On Multidimensional Variations (Gostekhizdat, Moscow, 1955) [in Russian].

    Google Scholar 

  31. Mathematical Encyclopedia (Soviet Encyclopedia, Moscow, 1977), Vols. 1–5 [in Russian].

  32. P. A. Chochia and O. P. Milukova, “Comparison of two-dimensional variations in the context of the digital image complexity assessment,” J. Commun. Technol. Electron. 60 (12), 1432–1440 (2015).

    Article  Google Scholar 

  33. A. S. Kronrod, “On functions of two variables,” Usp. Mat. Nauk 5 (1), 24–134 (1950) [in Russian].

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

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences, 127994, Moscow, Russia

    P. A. Chochia

Authors
  1. P. A. Chochia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. A. Chochia.

Additional information

Translated by E. Bondareva

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chochia, P.A. Contour-Constrained Image Smoothing Preserving Its Structure. J. Commun. Technol. Electron. 66, 769–777 (2021). https://doi.org/10.1134/S1064226921060073

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1064226921060073

Search

Navigation