This research paper proposes a filter to remove Random Valued
Impulse Noise (RVIN) based on Global Threshold Vector Outlyingness Ratio
(GTVOR) that is applicable for real time image processing. This filter works
with the algorithm that breaks the images into various decomposition levels
using Discrete Wavelet Transform (DWT) and searches for the noisy pixels using
the outlyingness of the pixel. This algorithm has the capability of
differentiating high frequency pixels and the “noisy pixel” using the threshold
as well as window adjustments. The damage and the loss of information are
prevented by means of interior mining. This global threshold based algorithm
uses different thresholds for different quadrants of DWT and thus helps in
recovery of noisy image even if it is 90% affected. Experimental results
exhibit that this method outperforms other existing methods for accurate noise
detection and removal, at the same time chain of connectivity is not
lost.
References
[1]
Gonzalez, R.C. and Woods, R.E. (2002) Digital Image Processing. Prentice-Hall, Englewood Cliffs.
[2]
Wang, Z. and Zhang, D. (1999) Progressive Switching Median Filter for the Removal of Impulse Noise from Highly Corrupted Images. IEEE Transactions on Circuits and Systems—II: Analog and Digital Signal Processing, 46, 78-80. http://dx.doi.org/10.1109/82.749102
[3]
Brownrigg, D.R.K. (1984) The Weighted Median Filter. Communications of the ACM, 27, 807-818. http://dx.doi.org/10.1145/358198.358222
[4]
Ko, S.J. and Lee, S.J. (1991) Center weighted Median Filters and Their Applications to Image Enhancement. IEEE Transactions on Circuits and Systems, 38, 984-993. http://dx.doi.org/10.1109/31.83870
[5]
Akkoul.S, Ledee, R., Leconge, R. and Harba, R. (2010) A New Adaptive Switching Median Filter. IEEE Signal Processing Letters, 17,587-590. http://dx.doi.org/10.1109/LSP.2010.2048646
[6]
Nikolova, M. (2004) Avariational Approach to Remove Outliers and Impulse Noise. Journal of Mathematical Imaging and Vision, 20, 99-120. http://dx.doi.org/10.1023/B:JMIV.0000011920.58935.9c
[7]
Chan, R.H., Hu, C. and Nikolova, M. (2004) An Iterative Procedure for Removing Random-Valued Impulse Noise. IEEE Signal Processing Letters, 11, 921-924. http://dx.doi.org/10.1109/LSP.2004.838190
[8]
Dong, Y., Chan, R.H. and Xu, S. (2007) A Detection Statistic for Random Valued Impulse Noise. IEEE Transactions on Image Processing, 16, 1112-1120. http://dx.doi.org/10.1109/TIP.2006.891348
[9]
Chan, R.H., Ho, C.W. and Nikolova, M. (2005) Salt-and-Pepper Noise Removal by Median-Type Noise Detectors and Detail Preserving Regularization. IEEE Transactions on Image Processing, 14, 1479-1485.
[10]
Huang, Y., Ng, M.K. and Wen, Y. (2009) Fast Image Restoration Methods for Impulse and Gaussian Noise Removal. IEEE Signal Processing Letters, 16, 457-460. http://dx.doi.org/10.1109/LSP.2009.2016835
[11]
Allard, W.K. (2008) Total Variation Regularization for Image Denoising. SIAM Journal on Imaging Sciences, 1, 400-417. http://dx.doi.org/10.1137/070698749
[12]
Wang, W. and Lu, P. (2011) An Efficient Switching Median Filter Based on Local Outlier Factor. IEEE Signal Processing Letters, 18, 551-554. http://dx.doi.org/10.1109/LSP.2011.2162583
[13]
Xiong, B. and Yin, Z. (2012) A Universal Denoising Framework with A New Impulse Detector and Nonlocal Means. IEEE Transactions on Image Processing, 21, 1663-1675. http://dx.doi.org/10.1109/TIP.2011.2172804
[14]
Breuig, M.M. (2000) LOF: Identifying Density-Based Local Outliers. Proceedings of the ACM SIGMOD Conference on Management of Data, Dallas, 15-18 May 2000, 93-104. http://dx.doi.org/10.1145/335191.335388
