Advertisement

Image De-Noising by Enhanced Median Filtering for High Density Noisy Images

  • Vikas Gupta
  • Abhishek Sharma
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 222)

Abstract

In the field of digital image processing [1], noise removal is always a critical process. In this paper we proposed an enhanced method of image de-noising. The purpose of this new method is to improve the signal to noise ratio (SNR) of de-noised image and get more better image, especially when image corrupted by high noise density. We improved the median filter algorithm, and get comparatively better results than previous methods. The mathematical analysis shows that this process improve the PSNR [2](Peak signal to noise ratio) at high density noise level. It also reduces the complexity of calculation because noise detection and noise removal both processes are performing simultaneously. This method produce better image without blurring and also preserve the edge and fine details of image.

Keywords

Median filter Threshold Peak signal to noise ratio Mean square error High density noise 

References

  1. 1.
    Gonzalez RC, Woods RE (2009) Digital image processing, 3rd edn. Pearson Prentice Hall, New JerseyGoogle Scholar
  2. 2.
    Nallaperumal K, Varghese J, Saudia S, Krishnaveni K, Mathew SP, Kumar P (2006) An efficient switching median filter for salt & pepper impulse noise reduction. In: 1st international conference on digital information management, 2006Google Scholar
  3. 3.
    Zhu Y, Huang C (2011) An improved median filtering algorithm combined with average filtering. IEEE third international conference on measuring technology and mechatronics automation, vol 6–7. Jan 2011, pp 420–423Google Scholar
  4. 4.
    Song T, Gabbouj M, Neuvo Y (1994) Center weighted median filters: some properties and applications in image processing. Signal Process 35(3):213–229CrossRefGoogle Scholar
  5. 5.
    Chen T, Wu H (2001) Adaptive impulse detection using centre-weighted median filters. Signal Process Lett 8(1):13CrossRefGoogle Scholar
  6. 6.
    Lin T-C, Yu P-T (2007) A new adaptive center weighted median filter for suppressing impulsive noise in images. Inf Sci 177:1073–1087CrossRefGoogle Scholar
  7. 7.
    Nallaperumal K, Varghese J, Saudia S, et.al. (2006) Selective switching median filter for the removal of salt & pepper impulse noise. I: Proceedings of IEEE WOCN 2006, Bangalore, IndiaGoogle Scholar
  8. 8.
    Wang Z, Zhang D (1999) Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Trans Circ Syst II: Analog Digital Signal Process 46(1):78–80CrossRefGoogle Scholar
  9. 9.
    Dong F, Fan H, Da Y (2010) A novel image median filtering algorithm based on incomplete quick sort algorithm. Int J Digital Content Technol Appl 4(6):79–84CrossRefGoogle Scholar
  10. 10.
    Xu X, Miller EL, Chen D, Sarhadi M (2004) Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. IEEE Trans Image Process 13(2):238–247CrossRefGoogle Scholar
  11. 11.
    Chang-You W, Fu-ping Y, Hui G (2010) A new kind of adaptive weighted median filter algorithm, In: International conference on computer application and system modeling (ICCASM 2010)Google Scholar
  12. 12.
    Forouzan AR, Araabi BN (2003) Iterative median filtering for restoration of images with impulsive noise. In: Proceedings of the 10th IEEE international conference on electronics, circuits and systems, 2003Google Scholar
  13. 13.
    Abreu E, Mitra SK (1995) A signal-dependent rank ordered mean (SD-ROM) filter. A new approach for removal of impulses from highly corrupted images. In: Proceedings of IEEE ICASSP-95, Detroit, MI, 1995, pp 2371–2374Google Scholar
  14. 14.
    Chen T, Ma K–K, Chen L-H (1999) Tri-state median filter for image denoising. IEEE Trans Image Process 8(12):1834–1838CrossRefGoogle Scholar
  15. 15.
    Behrooz G, Hadi SY, Faranak HY (2009) Modified adaptive center weighted median filter for suppressing impulsive noise in images. Int J Res Rev Appl Sci 1(3):218–228Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of Electronics and CommunicationTechnocrats institute of technologyBhopalIndia

Personalised recommendations