Open Access Open Access  Restricted Access Subscription or Fee Access

Removal of Impulse Noise using Adaptive Multilevel Filter

A. Radha, S. Athi Narayanan

Abstract


Image denoising is an important image processing task, both as a process itself, and as a component in other processes. The main properties of a good image denoising model are that it will remove noise while preserving edges. This paper presents a novel adaptive multilevel filter based on the cloud model (CM) to remove impulse noise; CM is an uncertain cognitive model called the CM filter. First, an uncertainty-based detector identifies the pixels corrupted by impulse noise. Then, a weighted multilevel arithmetic mean filter is applied to remove the noise candidates. Compared with the traditional filters, the CM filter makes a great improvement in image denoising.

Keywords


Cloud Model (CM), Image Denoising, Impulse Noise, Median Filter

Full Text:

PDF

References


Zhe Zhou, “Cognition and removal of impulse noise with uncertainty,” IEEE Trans. Image Process, vol. 21, no. 7, pp. 3157-3167, jul. 2012

S.-J. Ko and S.-J. Lee, “Center weighted median filters and applications to image enhancement,” IEEE Trans. Circuits Syst., vol. 38, no. 9, pp. 984–993, Sep. 1991.

L. Yin, R. Yang, M. Gabbouj, and Y. Neuvo, “Weighted median filters: A tutorial,” IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process. vol. 43, no. 3, pp. 157–192, Mar. 1996.

H. Hwang and R.A.Haddad, “Adaptive median filters: New algorithms and results,” IEEE Trans. Image Process., vol. 4, no. 4, pp. 499–502, Apr. 1995.

Abreu, M. Lightstone, S. K. Mitra, and K. Arakawa, “A new efficient approach for the removal of impulse noise from highly corrupted images,” IEEE Trans. Image Process., vol. 5, no. 6, pp. 1012–1025, Jun. 1996.

T. Chen, K.-K. Ma, and L.-H. Chen, “Tri-state median filter for image denoising,” IEEE Trans. Image Process., vol. 8, no. 12, pp. 1834–1838, Dec. 1999.

Z. Wang and D. Zhang, “Progressive switching median filter for the removal of impulse noise from highly corrupted images,” IEEE Trans.Circuits Syst. II, Analog Digit. Signal Process., vol. 46, no. 1, pp.78–80, Jan. 1999.

Zhang and Z. Wang, “Impulse noise detection and removal using fuzzy techniques,” Electron. Lett., vol. 33, pp. 378–379, Feb. 1997.

T. Chen and H. Wu, “Adaptive impulse detection using center weighted median filters,” IEEE Signal Process. Lett., vol. 8, no. 1, pp. 1–3, Jan. 2001.

H.-L. Eng and K.-K.Ma, “Noise adaptive soft-switching median filter,”IEEE Trans. Image Process., vol. 10, no. 2, pp. 242–251, Feb. 2001.

S. Zhang and M. A. Karim, “A new impulse detector for switchingmedian filters,” IEEE Signal Process. Lett., vol. 9, no. 11, pp. 360–363,Nov. 2002.

V. Crnojevic, V. Šenk, and Trpovski, “Advanced impulse detection based on pixel-wise MAD,” IEEE Signal Process. Lett., vol. 11, no. 7, pp. 589–592, Jul. 2004.

R. H. Chan, C.-W. Ho, and M. Nikolova, “Salt-and-pepper noise removal by the median-type noise detectors and their detail-preserving regularization,” IEEE Trans. Image Process., vol. 14, no. 10, pp. 1479–1485, Oct. 2005.

G. Pok, J.-C. Liu, and A. S. Nair, “Selective removal of impulse noise based on homogeneity level information,” IEEE Trans. Image Process., vol. 12, no. 1, pp. 85–92, Jan. 2003.

K. S. Srinivasan and D. Ebenezer, “A new fast and efficient decision based algorithm for removal of high-density impulse noises,” IEEE Signal Process. Lett., vol. 14, no. 3, pp. 189–192, Mar. 2007.

Y. Li, C. Y. Liu, and W. Y. Gan, “A new cognitive model: Cloud model,” Int. J. Intell. Syst., vol. 24, no. 3, pp. 357–375, Mar. 2009.

D. Y. Li and Y. Du, Artificial Intelligent With Uncertainty. Boca Raton, FL: CRC Press, 2007. May 1974.

E. Beaton and J. W. Tukey, “The fitting of power series, meaning polynomials, illustrated on band-spectroscopic data,” Technometricks, vol. 16, no. 2, pp. 147–185, May 1974.

Brownrigg, “The weighted median filter,” Commun. Assoc. Comput. vol. 27, no. 8, pp. 807–818, Aug. 1984.

T. Sun and Y.Neuvo, “Detail-preserving median based filters in image processing,” Pattern Recognit. Lett., vol. 15, no. 4, pp. 341–347, Apr. 1994.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.