De-noising Infrared Image Using OWA Based Filter
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.
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 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.
 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.
 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.
 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.
 D. Filev and R. R. Yager, “Analytic properties of maximum entropy OWA operators,” Information Science, vol. 85, pp. 11–27, 1995.
 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.
 R. Fuller and P. Majlender, “On obtaining minimal variability OWA operator weights,” Fuzzy Sets Syst., vol. 136, pp. 203–215, 2003.
 P. Majlender, “OWA operators with maximal Renyi entropy,” Fuzzy Sets Syst., vol. 155, pp. 340–360, 2005.
 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.
 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.
 A. Emrouznejad and G. R. Amin, “Improving minimax disparity model to determine the OWA operator weights,” Information Science, vol. 180, pp. 1477– 1485, 2010.
 D. H. Hong, “On proving the extended minimax disparity OWA problem,” Fuzzy Sets Syst., vol. 168, pp. 35–46, 2011.
 Y. M. Wang and C. Parkan, “A minimax disparity approach for obtaining OWA operator weights,” Information Science, vol. 175, pp. 20–29, 2005.
 G. R. Amin and A. Emrouznejad, “Parametric aggregation in ordered weighted averaging,” Int. J. Approx. Reason., vol. 52, pp. 819–827, 2011.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 H. Talebi and P. Milanfar, “Global Image De-noising”, IEEE Transaction Image Processing, vol. 23, no. 2, pp.755-768, Feb. 2014.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 Rafeal C. Gonzalez, Richard E. Wood, “Digital Image Processing Second Edition”, Prentice Hall Publication.