Abstract
The paper proposes a new numerical measure for filtering quality assessment of additive white Gaussian noise in digital images based on the analysis of closeness of the difference image to white noise. Such analysis is often conducted visually that leads to undesirable subjectivism. The numerical analysis of difference image using the properties of nonparametric BDS statistic was performed in this paper aimed at reducing the impact of subjectivism on the filtering quality assessment. The specified statistic is applied for the analysis of time sequence in testing the hypothesis on independence and identical distribution of its values. It can serve as a measure of quality of different filtering methods of noisy images. This statistic complements the toolkit of known practical measures of image quality, such as PSNR, MSE and SSIM. It is well known that a good quality of image filtering, from the viewpoint of these measures, not always corresponds to the better quality of filtering from the viewpoint of its visual perception. It has been shown that the measure using the values of BDS statistic demonstrates a high sensitivity to the structuring (dependence) of elements of difference image determined by the chosen filtering method. Using the simulation of image filtering algorithms implementing the methods of local and non-local filtering, a comparative analysis of their quality was conducted based on using BDS statistic.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
E. V. Osharovska, “Assessment of TV images quality attributes,” Digital Technologies, No. 19, 91 (2016). URI: https://ojs.onat.edu.ua/index.php/digitech/article/view/968 .
R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed. (Pearson, 2018).
M. A. Soto, J. A. Ramirez, and L. Thevenaz, “Optimizing image denoising for long-range Brillouin distributed fiber sensing,” J. Light. Technol. 36, No. 4, 1168 (Feb. 2018). DOI: https://doi.org/10.1109/JLT.2017.2750398 .
A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. II, pp. 60-65 (2005). DOI: https://doi.org/10.1109/CVPR.2005.38 .
A. Buades, B. Coll, and J. M. Morel, “Nonlocal image and movie denoising,” Int. J. Comput. Vis. 76, No. 2, 123 (Feb. 2008). DOI: https://doi.org/10.1007/s11263-007-0052-1 .
A. S. Lukin, M. V. Storozhylova, and D. V. Yurin, “Methods of analyzing the noise filtering quality of computer tomography images,” Proc. of 15-th Int. Conf. on Digital Signal Processing and its Application, DSPA’2013 (2013), pp. 85–88. URI: https://imaging.cs.msu.ru/en/publication?id=263 .
V. I. Vasylyshyn, “Adaptive variant of the surrogate data technology for enhancing the effectiveness of signal spectral analysis using eigenstructure methods,” Radioelectron. Commun. Syst. 58, No. 3, 116 (2015). DOI: https://doi.org/10.3103/S0735272715030036 .
E. Pirondini, A. Vybornova, M. Coscia, and D. Van De Ville, “A spectral method for generating surrogate graph signals,” IEEE Signal Process. Lett. 23, No. 9, 1275 (2016). DOI: https://doi.org/10.1109/LSP.2016.2594072 .
M. Small, Applied Nonlinear Time Series Analysis: Applications in Physics, Physiology and Finance (World Scientific Publishing, Singapore, 2005).
P. Yu. Kostenko, V. V. Slobodyanuk, O. V. Plahotenko, “Method of image filtering using singular decomposition and the surrogate data technology,” Radioelectron. Commun. Syst. 59, No. 9, 409 (2016). DOI: https://doi.org/10.3103/S0735272716090041 .
P. Yu. Kostenko, V. V. Slobodyanuk, I. L. Kostenko, “Method of image denoising in generalized phase space with improved indicator of spatial resolution,” Radioelectron. Commun. Syst. 62, No. 7, 368 (2019). DOI: https://doi.org/10.3103/S0735272719070045 .
L. Kanzler, “Very fast and correctly sized estimation of the BDS statistic,” SSRN Electron. J. (1999). DOI: https://doi.org/10.2139/ssrn.151669 .
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
ADDITIONAL INFORMATION
P. Yu. Kostenko, V. V. Slobodyanyuk, K. S. Vasiuta, and V. I. Vasylyshyn
The authors declare that they have no conflict of interest.
The initial version of this paper in Russian is published in the journal “Izvestiya Vysshikh Uchebnykh Zavedenii. Radioelektronika,” ISSN 2307-6011 (Online), ISSN 0021-3470 (Print) on the link http://radio.kpi.ua/article/view/S0021347020040032 with DOI: https://doi.org/10.20535/S0021347020040032
About this article
Cite this article
Kostenko, P.Y., Slobodyanyuk, V.V., Vasiuta, K.S. et al. Measure of Filtering Quality Assessment of Image Noise Using Nonparametric Statistic. Radioelectron.Commun.Syst. 63, 201–212 (2020). https://doi.org/10.3103/S0735272720040032
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S0735272720040032