Advertisement

A New Scoring Method for Directional Dominance in Images

Conference paper
  • 1.3k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10425)

Abstract

We aim to develop a scoring method for expressing directional dominance in the images. It is predicted that this score will give an information of how much improvement in system performance can be achieved when using a directional total variation (DTV)-based regularization instead of total variation (TV). For this purpose, a dataset consists of 85 images taken from the noise reduction datasets is used. The DTV values are calculated by using different sensitivities in the direction of the directional dominance of these images. The slope of these values is determined as the directional dominance score of the image. To verify this score, the noise reduction performances are examined by using direction invariant TV and DTV regulators of images. As a result, we observe that the directional dominance score and the improvement rate in noise reduction performance are correlated. Therefore, the resulting score can be used to estimate the performance of DTV method.

Keywords

Total Variation Directed Total Variation Directional Dominance Score 

Notes

Acknowledgment

This study is partially supported by The Scientific and Technological Research Council of Turkey with the grant number 115R285.

References

  1. 1.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Strong, D., Chan, T.: Edge-preserving and scale-dependent properties of total variation regularization. Inverse Prob. 19, S165–S187 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Bayram, İ., Kamasak, M.E.: Directional total variation. IEEE Signal Process. Lett. 19(12), 781–784 (2012)CrossRefGoogle Scholar
  4. 4.
    Beck, A., Teboulle, M.: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans. Image Process. 18, 2419–2434 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Yan, J., Lu, W.S.: Wu-Sheng: Image denoising by generalized total variation regularization and least squares fidelity. Multidimension. Syst. Signal Process. 26(1), 243–266 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Ritschl, L., Bergner, F., Fleischmann, C., Kachelrieß, M.: Improved total variation-based CT image reconstruction applied to clinical data. Phys. Med. Biol. 56(6), 1545–1561 (2011)CrossRefGoogle Scholar
  7. 7.
    Wang, Y., Yin, W., Zhang, Y.: A fast algorithm for image deblurring with total variation regularization (2007)Google Scholar
  8. 8.
    Liu, H., Gu, J., Huang, C.: Image deblurring by generalized total variation regularization and least squares fidelity. In: IEEE International Conference on Information and Automation (ICIA), pp. 1945–1949 (2016)Google Scholar
  9. 9.
    Lou, Y., Zeng, T., Osher, S., Xin, J.: A weighted difference of anisotropic and isotropic total variation model for image processing. SIAM J. Imaging Sci. 8(3), 1798–1823 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Demircan-Tureyen, E., Kamasak, M.E., Bayram, I.: Image reconstruction from sparse samples using directional total variation minimization. In: Proceedings of the 24th IEEE Signal Processing and Applications Conference (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Computer and Informatics EngineeringIstanbul Technical UniversityIstanbulTurkey

Personalised recommendations