Denoising of Noisy Pixels in Video by Neighborhood Correlation Filtering Algorithm

Full Text (PDF, 745KB), PP.61-67

Views: 0 Downloads: 0


P.Karunakaran 1,* S.Venkatraman 2 Hameem Shanavas .I 3 T.Kapilachander 4

1. Department of ECE, K.L.N College of Information Technology, Madurai , India

2. Department of ECE, Vel Tech Chennai, India

3. Department of ECE, M.V.J College of Engineering, Bangalore-67,India

4. Department of ECE, Sudharsan college Engineering, Trichy, India,

* Corresponding author.


Received: 24 Mar. 2012 / Revised: 9 May 2012 / Accepted: 13 Jun. 2012 / Published: 28 Jul. 2012

Index Terms

Video processing, Color Component Separation, Edge preserving, Partial noise blocks, Non linear filtering technique, Salt and Pepper noise, Speckle noise


A fast filtering algorithm for color video based on Neighborhood Correlation Filtering is presented. By utilizing a 3 × 3 pixel template, the algorithm can discriminate and filter various patterns of noise spots or blocks. In contrast with many kinds of median filtering algorithm, which may cause image blurring, it has much higher edge preserving ability. Furthermore, this algorithm is able to synchronously reflect image quality via amount, location and density statistics. Filtering of detected pixels is done by NCF algorithm based on a noise adaptive mean absolute difference. The experiments show that the proposed method outperforms other state-of-the-art filters both visually and in terms of objective quality measures such as the mean absolute error (MAE), the peak-signal-to-noise ratio (PSNR) and the normalized color difference (NCD).

Cite This Paper

P.Karunakaran,S.Venkatraman,I.Hameem Shanavas,T.Kapilachander,"Denoising of Noisy Pixels in Video by Neighborhood Correlation Filtering Algorithm", IJIGSP, vol.4, no.7, pp.61-67, 2012. DOI: 10.5815/ijigsp.2012.07.07


[1]S. Bacchelli and S. Papi, “Matrix thresholding for multiwavelet image denoising,” Numerical Algorithms, vol. 33, pp. 1–4, Aug. 2003.

[2]Srinivasan.K.S and Ebenezer.D, “A new fast and efficient Decision – Based algorithm for removal of high - density impulse noises”, IEEE signal processing letters, vol.14, no.3, 2007.

[3]Krishnan Nallaperumal, Justin Varghese, S.Saudia, K.Arulmozhi, K.Velu,S.Annam, “Salt & Pepper Impulse Noise Removal using Adaptive Switching Median Filter”, IEEE Transactions on Image Processing,2006.

[4]Kenny Kal Vin Toh, “Noise Adaptive Fuzzy Switching Median Filter for Salt-and-pepper Noise Reduction” IEEE Signal Processing Letters,Vol 17,No 3,March 2010.

[5]Fabrizio Russo ”New method for performance Evaluation of Grayscale Image Denoising Filters” IEEE Signal Processing Letters, Vol.17, No.5,May 2010.

[6]K.N. Plataniotis and A. N. Venetsanopoulos, “Color Image Processing and Applications”. Berlin, Germany: Springer, 2000.

[7]R. Lukac, “Adaptive vector median filtering,” Pattern Recognition. Lett, vol. 24, no. 12, pp. 1889–1899, Aug. 2003.

[8]M. Barni, V. Cappellini, and A. Mecocci, “Fast vector median filter based on Euclidean norm approximation,”IEEE Signal Process. Lett, vol. 1, no. 6, pp. 92–94, Jun. 1994.

[9]R. Lukac, K. N. Plataniotis, A. N. Venetsanopoulos, and B. Smolka, “A statistically-switched adaptive vector median filter,” J. Intell. Robot. Syst., vol. 42, no. 4, pp. 361–391, Apr. 2005.

[10]J. Camacho, S. Morillas, and P. Latorre, “Efficient impulse noise suppression based on statistical confidence limits,” J. Imag. Sci. Technol., vol. 50, no. 5, pp. 427–436, Sep. /Oct. 2006.

[11]S. Hore, B. Qiu, and H. R. Wu, “Improved vector filtering for color images using fuzzy noise detection,” Opt. Eng., vol. 42, no. 6, pp. 1656–1664, Jun. 2003.

[12]R. Lukac and K. N. Plataniotis, “A taxonomy of color image filtering and enhancement solutions,” Adv.Imag. Electron. Phys., vol. 140, pp. 187–264, June 2006.

[13]R. Lukac, K. N. Plataniotis, and A. N. Venetsanopoulos, “Color image denoising using evolutionary computation,” Int. J. Imag. Syst. Technol., vol. 15, no. 5, pp. 236–251, Oct. 2005.

[14]R. Lukac, K. N. Plataniotis, B. Smolka, and A. N. Venetsanopoulos, “cDNA microarray image processing using fuzzy vector filtering framework,” Fuzzy Sets Syst., vol. 152, no. 1, pp. 17–35, May 2005.

[15]R. Lukac, K. N. Plataniotis, B. Smolka, and A. N. Venetsanopoulos, “A multichannel order-statistic technique for cDNA microarray image processing,” IEEE Trans. Nanobiosci., vol. 3, no. 4, pp. 272–285, Dec. 2004.