De-noising Infrared Image Using OWA Based Filter
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De-noising Infrared Image Using OWA Based Filter

Authors: Ruchika, Munish Vashisht, S. Qamar

Abstract:

Detection of small ship is crucial task in many automatic surveillance systems which are employed for security of maritime boundaries of a country. To address this problem, image de-noising is technique to identify the target ship in between many other ships in the sea. Image de-noising technique needs to extract the ship’s image from sea background for the analysis as the ship’s image may submerge in the background and flooding waves. In this paper, a noise filter is presented that is based on fuzzy linguistic ‘most’ quantifier. Ordered weighted averaging (OWA) function is used to remove salt-pepper noise of ship’s image. Results obtained are in line with the results available by other well-known median filters and OWA based approach shows better performance.

Keywords: Linguistic quantifier, impulse noise, OWA filter, median filter.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1131483

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References:


[1] L. Zhang, W. Dong, D. Zhang, and G. Shi, “Two-stage image denoising by principal component analysis with local pixel grouping,” Pattern Recognition., vol. 43, no. 4, pp. 1531-5549, Apr. 2010.
[2] A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image de-noising,” in Proc. Conf. Computer Vision Pattern Recognition., vol. 2. pp. 60-65, 2005.
[3] R. R. Yager, “On ordered weighted averaging aggregation operators in multi criteria decision making,” IEEE Trans. Syst., Man, Cybern., vol. 18,no. 1, pp. 183–190, Jan./Feb. 1988.
[4] M. Elad and M. Aharon, “Image de-noising via sparse and redundant representations over learned dictionaries,” IEEE Transaction Image Processing, vol. 15, no. 12, pp. 3736-3745, Dec. 2006.
[5] D. Filev and R. R. Yager, “Analytic properties of maximum entropy OWA operators,” Information Science, vol. 85, pp. 11–27, 1995.
[6] M. O’Hagan, “Using maximum entropy-ordered weighted averaging to construct a fuzzy neuron,” in Proc. 24th Annu. IEEE Asilomar Conf. Signals Syst. Computer, Pacific Grove, CA, 1990, pp. 618–623.
[7] R. Fuller and P. Majlender, “On obtaining minimal variability OWA operator weights,” Fuzzy Sets Syst., vol. 136, pp. 203–215, 2003.
[8] P. Majlender, “OWA operators with maximal Renyi entropy,” Fuzzy Sets Syst., vol. 155, pp. 340–360, 2005.
[9] Y. M. Wang, Y. Luo, and X. Liu, “Two new models for determining OWA operator weights,” Computer Ind. Eng., vol. 52, pp. 203–209, 2007.
[10] G. R. Amin, “Note on A preemptive goal programming method for aggregating OWA operator weights in group decision making,” Information Science, vol. 177, pp. 3636–3638, 2007.
[11] A. Emrouznejad and G. R. Amin, “Improving minimax disparity model to determine the OWA operator weights,” Information Science, vol. 180, pp. 1477– 1485, 2010.
[12] D. H. Hong, “On proving the extended minimax disparity OWA problem,” Fuzzy Sets Syst., vol. 168, pp. 35–46, 2011.
[13] Y. M. Wang and C. Parkan, “A minimax disparity approach for obtaining OWA operator weights,” Information Science, vol. 175, pp. 20–29, 2005.
[14] G. R. Amin and A. Emrouznejad, “Parametric aggregation in ordered weighted averaging,” Int. J. Approx. Reason., vol. 52, pp. 819–827, 2011.
[15] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image De-noising by Sparse 3D Transform-Domain Collaborative Filtering,” IEEE Transaction Image Processing, vol. 16, no. 8, pp. 2080-2095, Aug. 2007.
[16] J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, “Non-local sparse models for image restoration,” in Proc. Int. Conf. Computer Vision, pp. 2272-2279, Sept. 29, 2009-Oct. 2, 2009.
[17] W. Dong, L. Zhang, and G. Shi, “Centralized sparse representation for image restoration,” in Proc. Int. Conf. Computer. Vision, pp. 1259-1266, 6-13 Nov. 2011.
[18] W. Dong, L. Zhang, G. Shi, and X. Li, “Nonlocally centralized sparse representation for image restoration,” IEEE Transaction Image Processing, vol. 22, no. 4, pp. 1620-1630, Apr. 2013.
[19] W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Transaction Image Processing, vol. 20, no. 7, pp. 1838-1857, Jul. 2011.
[20] J. Yang, J. Wright, T. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Transaction Image Processing, vol. 19, no. 11, pp. 2861-2873, Nov. 2010.
[21] Z. He, S. Yi, Y. Cheung, X. You, and Y. Tang, “Robust Object Tracking via Key Patch Sparse Representation”, IEEE Transactions on Cybernetics, no.99, pp.1-11, 2016.
[22] Francois G. Meyer and Xilin Shen, “Perturbation of the Eigenvectors of the Graph Laplacian: Application to Image Denoising”, Applied and Computational Harmonic Analysis, vol. 36, no. 2, pp. 326-334, 2014.
[23] H. Talebi and P. Milanfar, “Global Image De-noising”, IEEE Transaction Image Processing, vol. 23, no. 2, pp.755-768, Feb. 2014.
[24] M. Yuan and Y. Lin, “Model selection and estimation in regression with grouped variables,” J. Royal Stat. Soc., Ser. B, Stat. Methodology., vol. 68, no. 1, pp. 49-67, 2006.
[25] W. Zuo, L. Zhang, C. Song, D. Zhang, and H. Gao, “Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising,” IEEE Transaction Image Processing, vol. 23, no. 6, pp. 2459-2472, Jun. 2014.
[26] R. Garnett, T. Huegerich, C. Chui, and W. He, “A universal noise removal algorithm with an impulse detector,” IEEE Transaction Image Processing, vol. 14, no. 11, pp. 1747-1754, Nov. 2005.
[27] S. Schulte, M. Nachtegael, V. De Witte, D. Van der Weken, and E. E. Kerre, “A fuzzy impulse noise detection and reduction method,” IEEE Transaction Image Processing, vol. 15, no. 5, pp. 1153-1162, May. 2006.
[28] J. F. Cai, R. Chan, and M. Nikolova, “Two-phase methods for de-blurring images corrupted by impulse plus gaussian noise,” Inverse Problem Imaging., vol. 2, no. 2, pp. 187-204, 2008.
[29] J. Jiang, L. Zhang, and J. Yang, “Mixed Noise Removal by Weighted Encoding with Sparse Nonlocal Regularization,” IEEE Transaction Image Processing, vol. 23, no. 6, pp.2651-2662, Jun. 2014.
[30] J. Liu, X. C. Tai, H. Y. Huang, and Z. D. Huan, “A Weighted dictionary learning models for de-noising images corrupted by mixed noise,” IEEE Transaction Image Processing, vol. 22, no. 3, pp. 1108-1120, Mar. 2013.
[31] Rafeal C. Gonzalez, Richard E. Wood, “Digital Image Processing Second Edition”, Prentice Hall Publication.