Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Multi-denoising based impulse noise removal from images using robust statistical features and genetic programming

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Recently, several interesting computational intelligence based image denoising techniques have been reported for the removal of either salt & pepper or uniform impulse noise. However, to the best of our knowledge, the difficult challenge of developing a multi denoising method that can remove mixed-impulse noise, uniform impulse, salt & pepper, and impulse-burst noise, has not been reported so far. In this regard, we propose a new noise removal approach called INDE-GP for the removal of multi types of impulse noises. The proposed approach consists of two stages: noise detection stage and removal stage. At first, the impulse noise is localized by a single stage GP detector that exploits various information-rich, rank-ordered and robust statistical features for detection. Next the noise is removed only from the detected noisy pixels by single stage GP estimator. This estimator is developed by exploiting the global learning capability of GP and local statistical measures of noise-free pixels present in the neighborhood of noisy pixels. The experimental results and comparative analysis with existing denoising techniques show that multi denoising performance of the proposed INDE-GP approach is better both quantitative and qualitative ways.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Bo X, Zhouping Y (2012) A universal denoising framework with a new impulse detector and nonlocal means. IEEE Trans Image Process 21(4):1663–1675. doi:10.1109/TIP.2011.2172804

    Article  MathSciNet  Google Scholar 

  2. Brownrigg DRK (1984) The weighted median filter. Commun ACM 27(8):807–818. doi:10.1145/358198.358222

    Article  Google Scholar 

  3. Chan RH, Chung-Wa H, Nikolova M (2005) Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Trans Image Process 14(10):1479–1485. doi:10.1109/TIP.2005.852196

    Article  Google Scholar 

  4. Delon J, Desolneux A (2013) A patch-based approach for removing impulse or mixed gaussian-impulse noise. SIAM J Imaging Sci 6(2):1140–1174. doi:10.1137/120885000

    Article  MathSciNet  MATH  Google Scholar 

  5. Garnett R, Huegerich T, Chui C, Wenjie H (2005) A universal noise removal algorithm with an impulse detector. IEEE Trans Image Process 14(11):1747–1754. doi:10.1109/TIP.2005.857261

    Article  Google Scholar 

  6. Gonzalez RC, Woods RE (2006) Digital image processing. 3rd edn. Prentice-Hall, Inc.

  7. Hu H, Li B, Liu Q (2012) Non-local filter for removing a mixture of gaussian and impulse noises. In: VISAPP 2012 - Proceedings of the International Conference on Computer Vision Theory and Applications, Rome, Italy, pp 145–150

  8. Hwang H, Haddad R (1995) Adaptive median filters: new algorithms and results. IEEE Trans Image Process 4(4):499–502. doi:10.1109/83.370679

    Article  Google Scholar 

  9. Kaliraj G, Baskar S (2010) An efficient approach for the removal of impulse noise from the corrupted image using neural network based impulse detector. Image Vis Comput 28(3):458–466. doi:10.1016/j.imavis.2009.07.007

    Article  Google Scholar 

  10. Khan NU, Arya KV, Pattanaik M (2014) Edge preservation of impulse noise filtered images by improved anisotropic diffusion. Multimed Tools Appl 73(1):573–597. doi:10.1007/s11042-013-1620-8

    Article  Google Scholar 

  11. Koivisto P, Astola J, Lukin V, Melnik V, Tsymbal O (2003) Removing impulse bursts from images by training-based filtering. EURASIP J Appl Signal Process 2003:223–237. doi:10.1155/s1110865703211045

    Article  Google Scholar 

  12. Kong H, Guan L (1998) A noise-exclusive adaptive filtering framework for removing impulse noise in digital images. IEEE Trans Circ Syst II Analog Digit Signal Proc 45(3):422–428. doi:10.1109/82.664255

    Article  Google Scholar 

  13. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press

  14. Li B, Liu Q, Xu J, Luo X (2011) A new method for removing mixed noises. Sci China Inf Sci 54(1):51–59. doi:10.1007/s11432-010-4128-0

    Article  MATH  Google Scholar 

  15. Lin T-C (2010) Switching-based filter based on Dempster’s combination rule for image processing. Inf Sci 180(24):4892–4908. doi:10.1016/j.ins.2010.08.011

    Article  Google Scholar 

  16. Luo W (2006) An efficient detail-preserving approach for removing impulse noise in images. IEEE Signal Process Lett 13(7):413–416. doi:10.1109/LSP.2006.873144

    Article  Google Scholar 

  17. Mahmood MT, Majid A, Choi T-S (2011) Optimal depth estimation by combining focus measures using genetic programming. Inf Sci 181(7):1249–1263. doi:10.1016/j.ins.2010.11.039

    Article  Google Scholar 

  18. Majid A, Lee C-H, Mahmood M, Choi T-S (2012) Impulse noise filtering based on noise-free pixels using genetic programming. Knowl Inf Syst 32(3):505–526. doi:10.1007/s10115-011-0456-7

    Article  Google Scholar 

  19. Petrovic NI, Crnojevic XV (2008) Universal impulse noise filter based on genetic programming. IEEE Trans Image Process 17(7):1109–1120. doi:10.1109/TIP.2008.924388

    Article  MathSciNet  Google Scholar 

  20. Schowengerdt RA (2006) Remote sensing, 3rd Edition: Models and methods for Image processing. Academic Press, Inc.

  21. Searson DP, Leahy, D.E. Willis, M.J. (2010) GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. Paper presented at the Proceedings of the International MultiConference of Engineers and Computer Scientists 2010 (IMECS 2010), Hong Kong, 17–19

  22. Shuqun Z, Karim MA (2002) A new impulse detector for switching median filters. IEEE Signal Process Lett 9(11):360–363. doi:10.1109/LSP.2002.805310

    Article  Google Scholar 

  23. Singaravelan S, Murugan D (2013) Combined global–local specialized feature descriptor for content based image retrieval under noisy query. In: Advanced Computing and Communication Systems (ICACCS), 2013 International Conference on, 19–21 Dec. 2013 pp 1–6. doi:10.1109/ICACCS.2013.6938716

  24. Sun T, Neuvo Y (1994) Detail-preserving median based filters in image processing. Pattern Recogn Lett 15(4):341–347. doi:10.1016/0167-8655(94)90082-5

    Article  Google Scholar 

  25. Sung-Jea K, Yong-Hoon L (1991) Center weighted median filters and their applications to image enhancement. IEEE Trans Circ Syst 38(9):984–993. doi:10.1109/31.83870

    Article  Google Scholar 

  26. MATLAB 7.12 (2011). The MathWorks Inc., Natick, Massachusetts, United States

  27. Toprak A, Güler I (2007) Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter. Digit Signal Process 17(4):711–723. doi:10.1016/j.dsp.2006.11.008

    Article  Google Scholar 

  28. Treiber M (2010) An introduction to object recognition : selected algorithms for a wide variety of applications. Springer, London

    Book  MATH  Google Scholar 

  29. Tsymbal OV, Lukin VV, Koivisto PT, Melnik VP (2003) Removal of impulse bursts in satellite images. In: Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2003. Proceedings of the Second IEEE International Workshop on, 8–10 Sept. 2003 pp 324–329. doi:10.1109/IDAACS.2003.1249575

  30. Turkmen I (2013) A new method to remove random-valued impulse noise in images. Aeu-Int J Electron Commun 67(9):771–779. doi:10.1016/j.aeue.2013.03.006

    Article  MathSciNet  Google Scholar 

  31. Turkmen I (2014) Removing random-valued impulse noise in images using a neural network detector. Turk J Electr Eng Comput 22(3):637–649. doi:10.3906/Elk-1208-77

    Article  Google Scholar 

  32. Weber AG (1997) The USC-SIPI image database. 5 edn. University of Southern California, Signal and Image Processing Institute, Department of Electrical Engineering

  33. Yan M (2013) Restoration of images corrupted by impulse noise and mixed gaussian impulse noise using blind inpainting. SIAM J Imaging Sci 6(3):1227–1245. doi:10.1137/12087178X

    Article  MathSciNet  MATH  Google Scholar 

  34. Yiqiu D, Chan RH, Shufang X (2007) A detection statistic for random-valued impulse noise. IEEE Trans Image Process 16(4):1112–1120. doi:10.1109/TIP.2006.891348

    Article  MathSciNet  Google Scholar 

  35. Zang Q, Klette R (2003) Evaluation of an adaptive composite gaussian model in video surveillance. In: Petkov N, Westenberg M (eds) Computer analysis of images and patterns, vol 2756. Lecture notes in computer science. Springer Berlin Heidelberg, pp 165–172. doi: 10.1007/978-3-540-45179-2_21

  36. Zhang Y-J (2006) Advances in Image and Video Segmentation, vol Hershey, PA, USA. IGI Global: doi:10. 4018/978-1-59140-753-9

  37. Zhou W, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612. doi:10.1109/TIP.2003.819861

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by Higher Education Commission, Government of Pakistan under Indigenous PhD Fellowship Program-Batch VII, PIN No. 117-3250-EG7-012. The authors are also thankful to Dr. Dominic Searson for providing valuable information and help regarding GPTIPS.

Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), P.O. Nilore, Nilore, Islamabad, Pakistan

    Syed Gibran Javed, Abdul Majid & Asifullah Khan

  2. College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh, 11543, Kingdom of Saudi Arabia

    Anwar M. Mirza

Authors
  1. Syed Gibran Javed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdul Majid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anwar M. Mirza
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Asifullah Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Syed Gibran Javed.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 45 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Javed, S.G., Majid, A., Mirza, A.M. et al. Multi-denoising based impulse noise removal from images using robust statistical features and genetic programming. Multimed Tools Appl 75, 5887–5916 (2016). https://doi.org/10.1007/s11042-015-2554-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-2554-0

Keywords

Search

Navigation