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.
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References
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)
Strong, D., Chan, T.: Edge-preserving and scale-dependent properties of total variation regularization. Inverse Prob. 19, S165–S187 (2003)
Bayram, İ., Kamasak, M.E.: Directional total variation. IEEE Signal Process. Lett. 19(12), 781–784 (2012)
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)
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)
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)
Wang, Y., Yin, W., Zhang, Y.: A fast algorithm for image deblurring with total variation regularization (2007)
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)
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)
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)
Acknowledgment
This study is partially supported by The Scientific and Technological Research Council of Turkey with the grant number 115R285.
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Akkoca-Gazioglu, B.S., Kamasak, M. (2017). A New Scoring Method for Directional Dominance in Images. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_1
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DOI: https://doi.org/10.1007/978-3-319-64698-5_1
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