A Global-Local Noise Removal Approach to Remove High Density Impulse Noise
Subject Areas : Signal Processing
Ali Mohammad Fotouhi
1
(Electrical Engineering)
Samane Abdoli
2
(Tafresh University)
Vahid Keshavarzi
3
(Tafresh University)
Keywords: Impulse noise , noise detection , noise removal , adaptive median filter,
Abstract :
Impulse noise removal from images is one of the most important concerns in digital image processing. Noise must be removed in a way that the main and important information of image is kept. Traditionally, the median filter has been the best way to deal with impulse noise; however, the image quality obtained in high noise density is not desirable. The aim of this paper is to propose an algorithm in order to improve the performance of adaptive median filter to remove high density impulse noise from digital images. The proposed method consists of two main stages of noise detection and noise removal. In the first stage, noise detection includes two global and local phases and in the second stage, noise removal is also done based on a two-phase algorithm. Global noise detection is done by a pixel classification approach in each block of the image and local noise detection is performed by automatically determining two threshold values in each block. In the noise removal stage only noisy pixels detected from the first stage of the algorithm are processed by estimating noise density and applying adaptive median filter on noise-free pixels in the neighborhood. Comparing experimental results obtained on standard images with other proposed methods proves the success of the proposed algorithm.
[1] H. Ibrahim, N. S. P. kong and Theam Foo Ng , “Simple adaptive median filter for the removal of impulse noise from highly corrupted images”, IEEE Transactions on Consumer Electronics, vol. 54, pp. 1920 - 1927, 2008. #
[2] A. Jourabloo, A. H. Feghahati, M. Jamzad, “New algorithms for recovering highly corrupted images with impulse noise”, Scientia Iranica, vol. 19, Issue 6, pp. 1738–1745, Dec. 2012. #
[3] H. Hwang and R. A. Haddad, “Adaptive median filters: New algorithms and results”, IEEE Transactions on Image Processing, vol. 4, pp. 499 - 502, 1995. #
[4] K. S. Srinivasan, D. Ebenezer, “A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises”, IEEE Signal Processing Letters, vol. 14, Issue 3, pp. 189 – 192, March 2007. #
[5] Lin Yin, Ruikang Yang, M. Gabbouj, Y. Neuvo, “Weighted Median Filters: A Tutorial”, IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processin, vol. 43, Issue 3, pp. 157 – 192, Mar 1996. #
[6] T. Sun and Y. Neuvo, “Detail-presserving median based filters in image processing”, Pattern Recognition Letters, vol. 15, pp. 341-347, 1994. #
[7] H. G. Senel, R. A. Peters and B. Dawant, “Topological median filters”, IEEE Transactions on Image Processing , vol. 11, pp. 89-104, 2002. #
[8] Z. Wang and D. Zhang, “Progressive switching median filter for the removal of impulse noise from highly corrupted images”, IEEE Transactions on Circuits and systems, vol. 46, pp. 78 – 80, 1999. #
[9] S. Zhang and M. A. Karim, “A new impulse detector for switching median filters”, IEEE Signal Processing Letters, vol. 9, pp. 360 - 363, 2002. #
[10] G. Pok, J. C. Liu and A. S. Nair, “Selective removal of impulse noise based on homogeneity level information”, IEEE Transactions on Image processing, vol. 12, pp. 85-92, 2003.#
[11] W. Luo, “Efficient removal of impulse noise from digital images”, IEEE Transactions on Consumer Electronics, vol. 52, pp. 523 – 527, 2006. #
[12] S. Yuan and Y. Tan, “Impulse noise removal by a global-local noise detector and adaptive median filter”, Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs, vol. 86, pp. 2123-2128, 2006. #
[13] P. Saikrishna and P. K. Bora, “Detection and removal of fixed-valued impulse noise using sparse representations”, IEEE Signal Processing and Communications (SPCOM), 2012 International Conference on, pp. 1 - 5, 2012. #
[14] M. Waqas, S. G. Javed, and A. Khan, “Random-valued impulse noise removal from images: K-means and luo-statistics based detector and nonlocal means based estimator”, in Applied Sciences and Technology (IBCAST), 2014 11th International Bhurban Conference on, pp. 130-135, 2014. #
[15] J. Qiao, L. Chen, and Y. Chen, “A method for wide density salt and pepper noise removal”, 26th Chinese Conference on Control and Decision, pp. 2940-2943, 2014. #
[16] V. Gupta, V. Chaurasia, M. Shandilya, “Random-valued impulse noise removal using adaptive dual threshold median filter”, Journal of Visual Communication and Image Representation,vol. 26, pp. 296-304, Jan. 2015. #
[17] I. Turtmen, “The ANN based detector to remove random-valued impulse noise in images”, Journal of Visual Communication and Image Representation, Vol. 34, pp. 28-36, Jan. 2016. #
[18] H. L. Eng and K. K. Ma. “Noise adaptive soft switching median filter”, IEEE Transactions on Image Processing , vol. 10, pp. 242-251, 2001.#