Advertisement

Edge preserving mixed noise removal

  • Fenghua Guo
  • Caiming Zhang
Article
  • 27 Downloads

Abstract

To faithfully recover the clean images corrupted by additive white Gaussian noise (AWGN) and impulse noise (IN), a novel edge preserving image denoising algorithm is proposed. The low- and high-frequency components of the image are restored separately. The high-frequency components of the images are restored based on nonlocal self-similarity (NSS) learning from natural images. An energy minimization function is developed to combine the low- and high-frequency components into one model. Experiments demonstrate that the proposed method outperforms existing mixture noise removal methods in peak signal-to-noise ratio (PSNR), edges preservation and visual performance.

Keywords

Mixed Noise Removal Edge Preserving High-frequency Components 

Notes

Acknowledgements

The authors would like to thank the reviewers for their invaluable comments. This work was supported partly by National Natural Science Foundation of China (No. 61772312, 61602277), NSFC Joint Fund with Zhejiang Integration of Informatization and Industrialization under Key Project(U1609218) and Natural Science Foundation of Shandong Province, China (No. ZR2017MF033).

References

  1. 1.
    Aharon M, Elad M, Bruckstein AM (2006) K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans Signal Processing 54(11):4311–4322zbMATHCrossRefGoogle Scholar
  2. 2.
    Bovik AC (2010) Handbook of image and video processing. Academic pressGoogle Scholar
  3. 3.
    Brownrigg D (1984) The weighted median filter. Commun ACM 27(8):807–818CrossRefGoogle Scholar
  4. 4.
    Buades A, Coll B, Morel J-M (2005) A non-local algorithm for image denoising. IEEE Computer Society Conference on Computer Vision (CVPR), San Diegopp. 60–65Google Scholar
  5. 5.
    Cai J, Chan RH, Nikolova M (2008) Two-phase approach for deblurring images corrupted by impulse plus gaussian noise. Inverse Problem Imag 2(2):187–204MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Chan RH, Ho C-W, Nikolova M (2005) Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Trans Image Process 14(10):1479–1485CrossRefGoogle Scholar
  7. 7.
    Chang H, Yeung DY, Xiong YM (2014) Super resolution through neighbor embedding. Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, pp.275–282Google Scholar
  8. 8.
    Chen C, Liu L, Chen L, Tang Y, Zhou Y (2015) Weighted couple sparse representation with classified regularization for impulse noise removal. IEEE Trans Image Process 24(11):4014–4026MathSciNetCrossRefGoogle Scholar
  9. 9.
    Chen T, Ma KK, Chen LH (1999) Tri-state median filter for image denosing. IEEE Trans Image Process 8(12):1834–1838CrossRefGoogle Scholar
  10. 10.
    Chen T, Wu HR (2001) Adaptive impulse detection using center weighted median filters. IEEE Signal Process Lett 8(1):1–3CrossRefGoogle Scholar
  11. 11.
    Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans Image Process 16(8):2080–2095MathSciNetCrossRefGoogle Scholar
  12. 12.
    Dong YQ, Xu SF (2007) A new directional weighted median filter for removal of random-valued impulse noise. IEEE Signal Process Lett 14(3):193–196CrossRefGoogle Scholar
  13. 13.
    Dong W, Zhang L, Shi G, Li X (2013) Nonlocally centralized sparse representation for image restoration. IEEE Trans Image Process 22(4):1620–1630MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Gilboa G, Osher S (2008) Nonlocal operators with applications to image processing. Multisc Model Simul 7(3):1005–1028MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Gu S, Zhang L, Zuo W, Feng X (2017) Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision. Int J Comput Vis 121(2):183–208CrossRefGoogle Scholar
  16. 16.
    Guo F, Zhang C, Zhang M (2018) Edge-preserving image denoising. IET Image Process 12(8):1394–1401CrossRefGoogle Scholar
  17. 17.
    Huang T, Dong W, Xie X, Shi G, Bai X (2017) Mixed Noise Removal via Laplacian Scale Mixture Modeling and Nonlocal Low-rank Approximation. IEEE Trans Image Process 26(7):3171–3186MathSciNetCrossRefGoogle Scholar
  18. 18.
    Hwang H, Haddad RA (1995) Adaptive median filters: New algorithm and results. IEEE Trans Image Process 4(4):499–502CrossRefGoogle Scholar
  19. 19.
    Ji H, Huang S, Shen Z, Xu Y (2011) Robust video restoration by joint sparse and low rank matrix approximation. SIAM J Imag Sci 4(4):1122–1142MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Ji H, Liu C, Shen Z, Xu Y (2010) Robust video denoising using low rank matrix completion. In Proc. IEEE Conf. on Comput. Vis. Pattern Recognit., pp. 1791–1798Google Scholar
  21. 21.
    Jiang J, Zhang L, Yang J (2014) Mixed noise removal by weighted encoding with sparse nonlocal regularization. IEEE Trans Image Process 23(6):2651–2662MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    Ko SJ, Lee YH (1991) Center weighted median filters and their applications to image enhancement. IEEE Trans Circuits syst 38(9):984–993CrossRefGoogle Scholar
  23. 23.
    Li Y, Liu J, Yang W, Guo Z (2015) Neighborhood regression for edge-preserving image super-resolution. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1201–1205Google Scholar
  24. 24.
    Li Y, Shen L, Dai D, Suter B (2011) Framelet algorithms for de-blurring images corrupted by impulse plus gaussian noise. IEEE Trans Image Process 20(7):1822–1837MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2activity: Recognizing complex activities from sensor data. In: Proceedings of the 24th International Conference on Artificial Intelligence ( IJCAI'15), pp. 1617–1623, Buenos AiresGoogle Scholar
  26. 26.
    Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115CrossRefGoogle Scholar
  27. 27.
    Liu J, Tai X, Huang H, Huan Z (2013) A weighted dictionary learning model for denoising images corrupted by mixed noise. IEEE Trans Image Process 22(3):1108–1120MathSciNetzbMATHCrossRefGoogle Scholar
  28. 28.
    Liu Y, Zhang L, Nie L,Yan Y, Rosenblum DS (2016) Fortune teller: Predicting your career path. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), pp.201–207Google Scholar
  29. 29.
    Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A (2009) Nonlocal sparse models for image restoration. IEEE International Conference on Computer Vision (ICCV), pp. 2272–2279Google Scholar
  30. 30.
    Muja M, Lowe DG (2014) Scalable Nearest Neighbor Algorithms for High Dimensional Data. Pattern Analysis and Machine Intelligence 36(11):2227–2240CrossRefGoogle Scholar
  31. 31.
    Nieminen A, Heinonen P, Neuvo Y (1987) A new class of detail preserving filters for image processing. IEEE Trans Pattern Anal Mach Intell 9(1):74–90CrossRefGoogle Scholar
  32. 32.
    Nikolova M (2004) A variational approach to remove outliers and impulse noise. J Math Imag Vis 20:99–120MathSciNetzbMATHCrossRefGoogle Scholar
  33. 33.
    Pok G, Liu JC, Nair AS (2003) Selective removal of impulse noise based on homogeneity level information. IEEE Trans Image Process 12(1):85–92CrossRefGoogle Scholar
  34. 34.
    Rodríguez P, Rojas R, Wohlberg B (2012) Mixed gaussian-impulse noise image restoration via total variation. In Proc. IEEE. Int. Conf. Acoust. Speech Signal Process., pp. 1077–1080Google Scholar
  35. 35.
    Sun T, Neuvo Y (1994) Detail-preserving median based filters in image processing. Pattern Recogn Lett 15(4):341–347CrossRefGoogle Scholar
  36. 36.
    Timofte R, Smet VD, Gool LV (2013) Anchored neighborhood regression for fast example based super-resolution. IEEE International Conference on Computer Vision (ICCV), pp. 1920–1927Google Scholar
  37. 37.
    Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. IEEE International Conference on Computer Vision (ICCV), pp. 839–846Google Scholar
  38. 38.
    Xiao Y, Zeng T, Yu J, Ng M (2011) Restoration of images corrupted by mixed gaussian-impulse noise via l1–l0 minimization. Pattern Recogn 44(8):1708–1720zbMATHCrossRefGoogle Scholar
  39. 39.
    Xu J, Zhang L, Zuo W, Zhang D, Feng X (2015) Patch group based nonlocal self-similarity prior learning for image denoising. IEEE International Conference on Computer Vision (ICCV), pp. 244–252Google Scholar
  40. 40.
    Yan M (2013) Restoration of images corrupted by impulse noise and mixed gaussian impulse noise using blind inpainting. SIAM J Imag Sci 6(3):1227–1245MathSciNetzbMATHCrossRefGoogle Scholar
  41. 41.
    Zhang M, Desrosiers C (2017) Image denoising based on sparse representation and gradient histogram. IET Image Process 11(1):54–63CrossRefGoogle Scholar
  42. 42.
    Zhang L, Zhang L, Mou X, andZhang D (2011) Fsim: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386MathSciNetzbMATHCrossRefGoogle Scholar
  43. 43.
    Zhang F, Zhang X, Qin XY, Zhang CM (2015) Enlarging Image by Constrained Least Square Approach with Shape Preserving. J Comput Sci Technol 30(3):489–498MathSciNetCrossRefGoogle Scholar
  44. 44.
    Zhu Y, Zhang YN, Alan L, Yuille AL (2014) Single image super-resolution using deformable patches. Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, pp. 2917–2924Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of SoftwareShandong UniversityJinanPeople’s Republic of China

Personalised recommendations