Research of sparse representation method for ringing suppression
A.V. Umnov, A.S. Krylov


National Research University Higher School of Economics, Moscow, Russia,
Lomonosov Moscow State University, Moscow, Russia

Full text of article: Russian language.


In this paper we suggest an algorithm for ringing suppression based on a sparse representation method. As one of its steps, the suggested method includes image deblurring based on the Wiener-Hunt deconvolution algorithm. The ringing suppression algorithm uses the signals' mutual coherence and sparsities analysis when dealing with the ringing effect based on the sparse representation method. We also analyze the mutual coherence and sparsities for blurred images and images with white Gaussian noise.

ringing effect, sparse representations, mutual coherence.

Umnov AV, Krylov AS. Research of sparse representation method for ringing suppression. Computer Optics 2016; 40(6): 895-903. DOI: 10.18287/2412-6179-2016-40-6-895-903.


  1. Nasonov AV, Krylov AS. Edge quality metrics for image enhancement. Pattern Recognition and Image Analysis 2012; 22(2): 346-353. DOI: 10.1134/S1054661812020113.
  2. Koh CC, Mitra SK, Foley JM, Heynderickx I. Annoyance of individual artifacts in MPEG-2 compressed video and their relation to overall annoyance. Proc SPIE 2005; 5666: 595-606. DOI: 10.1117/12.587423.
  3. Liu H, Klomp N, Heynderickx I. A perceptually relevant approach to ringing region detection. IEEE Transactions on Image Processing 2010; 19(6): 1414-1426. DOI: 10.1109/TIP.2010.2041406.
  4. Marziliano P, Dufaux S, Winkler T, Ebrahimi T. Perceptual blur and ringing metrics: application to JPEG2000. Signal Processing: Image Communication 2005; 19: 163-172. DOI: 10.1016/j.image.2003.08.003.
  5. Chang H, Ng MK, Zeng T. Reducing artifacts in JPEG decompression via a learned dictionary. IEEE Transactions on Signal Processing 2014; 62(3): 718-728. DOI: 10.1109/TSP.2013.2290508.
  6. Shen M-Y, Jay Kuo CC. Review of Postprocessing Techniques for Compression Artifact Removal. Journal of Visual Communication and Image Representation 1998; 9(1): 2-14. DOI: 10.1006/jvci.1997.0378.
  7. Mosleh A, Langlois JMP, Green P. Image deconvolution ringing artifact detection and removal via psf frequency analysis. ECCV 2014: Lecture Notes in Computer Science 2014; 8692: 247-262. DOI: 10.1007/978-3-319-10593-2_17.
  8. Umnov A, Nasonov A, Krylov A, Yong D. Sparse method for ringing artifact detection. ICOSP 2014: 662-667. DOI: 10.1109/ICOSP.2014.7015086.
  9. Nasonov A, Krylov A. Adaptive image deringing. Proceedings of GraphiCon 2009: 151-154.
  10. Krylov A, Sitdikov I. Variational image deringing using varying regularization parameter. Pattern Recognition and Image Analysis 2015; 25(1): 96-100. DOI: 10.1134/S1054661815010186.
  11. Kellner E, Dhital B, Kiselev V, Reisert M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magnetic resonance in medicine 2016; 76(5): 1574-1581. DOI: 10.1002/mrm.26054.
  12. Mallat S. A wavelet tour of signal processing: the sparse way. Philadelphia: Elseveir; 2009. ISBN: 978-0-12-374370-1.
  13. Rudin L, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 1992; 60(1-4): 259-268. DOI: 0.1016/0167-2789(92)90242-F.
  14. Umnov A, Krylov A, Nasonov A. Ringing artifact suppression using sparse representation. ACIVS 2015: 35-45. DOI: 10.1007/978-3-319-25903-1_4.
  15. Elad M. Sparse and redundant representations. New York: Springer; 2010. ISBN: 978-1441970107.
  16. Elad M, Figueiredo MAT, Ma Y. On the role of the sparse and redundant representations in image processing. Proc IEEE 2010; 98(6): 972-982. DOI: 10.1109/JPROC.2009.2037655.
  17. Koltsov PP. Image blur estimation. Computer Optics 2011; 35(1): 95-102.
  18. Orieux F, Giovannelli JF, Rodet T. Bayesian estimation of regularization and point spread function parameters for Wiener-Hunt deconvolution. JOSA A 2010; 27(7): 1593-1607. DOI: 10.1364/JOSAA.27.001593.
  19. Donoho DL, Elad M, Temlyakov VN. Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Transactions on Information Theory 2006; 52(1): 6-18. DOI: 10.1109/TIT.2005.860430.
  20. Krylov AS, Umnov AV. Influence of Gibbs phenomenon on the mutual coherence in sparse representations [In Russian]. Moscow University Bulletin. Series 15: Computational Mathematics and Cybernetics 2016; 4: 12-17.
  21. Ringing analysis database. Source: áhttp://ima­ñ.
  22. Jones E, Oliphant E, Peterson P, et al. SciPy: Open Source scientific tools for Python. Source: áhttp://scipy.orgñ.
  23. Hunter J. Matplotlib: A 2D graphics environment. Computing in Science and Engineering 2007; 9(3): 90-95. DOI: 10.1109/MCSE.2007.55.
  24. Pedregosa F, et al. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 2011; 12: 2825-2830.

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