Filtering Random Valued Impulse Noise from Grayscale Images through Support Vector Machine and Markov Chain

[1] F. Agostinelli, M.R. Anderson, H. Lee, Adaptive multi-column deep neural networks with application to robust image denosing, Advances in Neural Information Processing Systems 26, pp. 1493–1501, Lake Tahoe, NV, USA, 2013. Search in Google Scholar

[2] I. Aizenberg, G. Wallace, Intelligent detection of impulse noise using multilayer neural network with multi-valued neurons, SPIE Proceedings, Vol. 8295, p. 82950S, 2012.10.1117/12.907639 Search in Google Scholar

[3] S. Banerjee, A. Bandyopadhyay, A. Mukherjee, A. Das, and R. Bag, Random Valued Impulse Noise Removal Using Region Based Detection Approach, Engineering, Technology & Applied Science Research, Vol. 7, No. 6, pp. 2288–2292, 2017.10.48084/etasr.1609 Search in Google Scholar

[4] A. Buades, B. Coll, J.-M. Morel, A non-local algorithm for image denosing, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 60–65, San Diego, CA, USA, 2005. Search in Google Scholar

[5] F. Estrada, D. Fleet, A. Jepson, Stochastic image denoising, British Machine Vision Conference, p. 117, London, 2009.10.5244/C.23.117 Search in Google Scholar

[6] A. Gellert, R. Brad, Context-Based Prediction Filtering of Impulse Noise Images, IET Image Processing, Vol. 10, Issue 6, pp. 429–437, 2016.10.1049/iet-ipr.2015.0702 Search in Google Scholar

[7] A. Gellert, R. Brad, Studying the influence of search rule and context shape in filtering impulse noise images with Markov chains, Signal, Image and Video Processing, Springer London, Vol. 12, Issue 2, pp. 315–322, 2018.10.1007/s11760-017-1160-1 Search in Google Scholar

[8] A. Gellert, R. Brad, Image Inpainting with Markov Chains, Signal, Image and Video Processing, Vol. 14, Issue 7, pp. 1335–1343, 2020.10.1007/s11760-020-01675-7 Search in Google Scholar

[9] A. Gellert, A. Florea, Investigating a New Design Pattern for Efficient Implementation of Prediction Algorithms, Journal of Digital Information Management, Vol. 11, Issue 5, pp. 366–377, 2013. Search in Google Scholar

[10] A.B. Hamza, P. Luque-Escamilla, J. Martínez-Aroza, R. Román-Roldán, Removing noise and preserving details with relaxed median filters, Journal of Mathematical Imaging and Vision, Vol. 11, Issue 2, pp. 161–177, 1999.10.1023/A:1008395514426 Search in Google Scholar

[11] N. Iqbal, S. Ali, I. Khan, B. M. Lee, Adaptive Edge Preserving Weighted Mean Filter for Removing Random-Valued Impulse Noise, Symmetry, Vol. 3, Issue 11, 2019.10.3390/sym11030395 Search in Google Scholar

[12] Q. Jin, L. Bai, J. Yang, I. Grama, Q. Liu, A New Method for Removing Random-Valued Impulse Noise, Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, Vol. 8836. Springer, Cham, 2014.10.1007/978-3-319-12643-2_2 Search in Google Scholar

[13] C. Junqing, Z. Guizhen, X. Shaoping, Y. Haiwen, A Blind CNN Denoising Model for Random-Valued Impulse Noise, IEEE Access, Vol. 7, pp. 124647–124661, 2019.10.1109/ACCESS.2019.2938799 Search in Google Scholar

[14] R. Kunsoth, M. Biswas, Modified decision based median filter for impulse noise removal, 2016 International Conference on Wireless Communications, Signal Processing and Networking, pp. 1316–1319, 2016.10.1109/WiSPNET.2016.7566350 Search in Google Scholar

[15] T.C. Lin, SVM-based filter using evidence theory and neural network for image denoising, Journal of Software Engineering and Applications, Vol. 6, Issue 3B, pp. 106–110, 2013.10.4236/jsea.2013.63B023 Search in Google Scholar

[16] A. Majumdar, R.K. Ward, Synthesis and Analysis Prior Algorithms for Joint-Sparse Recovery, IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3421–3424, 2012.10.1109/ICASSP.2012.6288651 Search in Google Scholar

[17] J. Matsuoka, T. Koga, N. Suetake and E. Uchino, Random-valued impulse noise removal in color images by using switching non-local vector median filter, 2015 International Symposium on Intelligent Signal Processing and Communication Systems, pp. 11–16, 2015.10.1109/ISPACS.2015.7432727 Search in Google Scholar

[18] S.K. Mishra, G. Panda, S. Meher, Chebyshev functional link artificial neural networks for denosing of image corrupted by salt and pepper noise, International Journal of Recent Trends in Engineering, Vol. 1, No. 1, pp. 42–46, 2010. Search in Google Scholar

[19] M. Nadeem, A. Hussain, A. Munir, M. Habib, M.T. Naseem, Removal of random valued impulse noise from grayscale images using quadrant based spatially adaptive fuzzy filter, Signal Processing, Volume 169, p. 107403, 2020.10.1016/j.sigpro.2019.107403 Search in Google Scholar

[20] C. Nello, J. Swawe-Taylor, An introduction to Support Vector Machines, Cambridge University Press, 2000. Search in Google Scholar

[21] B. Schoslkopf, A. Smola, Learning with Kernels, MIT Press, London, 2002. Search in Google Scholar

[22] P.L.B. Soares, J.P. Silva, Neural networks applied for impulse noise reduction from digital images, INFOCOMP J. Comput. Sci., Vol. 11, Issue 3–4, pp. 7–14, 2012. Search in Google Scholar

[23] K.S. Srinivasan, D. Ebenezer, A New Fast and Efficient Decision Based Algorithm for Removal of High Density Impulse Noise, IEEE Signal Processing Letters, Vol. 14, Issue 3, pp. 189–192, 2007.10.1109/LSP.2006.884018 Search in Google Scholar

[24] K.K.V. Toh, N.A.M. Isa, Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction, IEEE Signal Processing Letters, Vol. 17, Issue 3, pp. 281–284, 2010.10.1109/LSP.2009.2038769 Search in Google Scholar

[25] I. Türkmen, Removing random-valued impulse noise in images using neural network detector, Turkish Journal of Electrical Engineering and Computer Science, Vol. 22, Issue 3, pp. 637–649, 2014.10.3906/elk-1208-77 Search in Google Scholar

[26] Z. Wang, D. Zhang, 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, Vol. 46, Issue 1, pp. 78–80, 1999.10.1109/82.749102 Search in Google Scholar

[27] G. Wang, D. Li, W. Pan, Z. Zang, Modified switching median filter for impulse noise removal, Signal Processing, Vol. 90, Issue 12, pp. 3213–3218, 2010.10.1016/j.sigpro.2010.05.026 Search in Google Scholar

[28] A. Wong, A. Mishra, W. Zhang, P. Fieguth, D.A. Clausi, Stochastic image denoising based on Markov-chain Monte Carlo sampling, Signal Processing, Vol. 91, Issue 8, pp. 2112–2120, 2011.10.1016/j.sigpro.2011.03.021 Search in Google Scholar

[29] Z. Zhu, X. Zhang, A random-valued impulse noise removal algorithm via just noticeable difference threshold detector and weighted variation method, International Journal of Computers and Applications, 2020.10.1080/1206212X.2020.1719309 Search in Google Scholar

[30] https://www.csie.ntu.edu.tw/~cjlin/libsvm/ Search in Google Scholar