Author : Kanike Vijay Kumar 1
Date of Publication :7th June 2016
Abstract: Aiming at the problem of ground penetrating radar image de-noising, a new adaptive image de-noising algorithm based on non sub sampled Contour let transform is proposed. The algorithm firstly performs non sub sampled Contour let transform to the noise image, to obtain the coefficients of each directional sub band and each scale, then, according to the energy of the coefficient, the de-noising threshold value is adjusted adaptively. Simulation results show that, compared with the wavelet threshold de-noising algorithm, the proposed algorithm can effectively remove the Gauss white noise in the image, improve the peak signal to noise ratio (PNSR), while preserving the edge details of the image, it can improve the PSNR value and reduce the Gibbs phenomenon.
Reference :
-
[1] Jiawei Yuan; Shucheng Yu. ”Privacy Preserving BackPropagation NeuralNetwork Learning Made Practical with Cloud Computing,” IEEE Transaction paper on parallel and distributedsystem, digital object identifier 2013 .
[2] T. Chen and S. Zhong. ”Privacy-preserving backpropagation neural network learning”. Trans. Neur. Netw., 20(10):15541564, Oct. 2009
[3] N. Schlitter. ”A protocol for privacy preserving neural network learning on horizontal partitioned data”. In Proceedings of the Privacy Statistics in Databases (PSD), Sep. 2008.
[4] D. Boneh, E.-J. Goh, and K. Nissim. ”Evaluating 2-dnf formulas on ciphertexts”. In Proceedings of the Second international conference on Theory of Cryptography, TCC05, pages 325341, Berlin, Heidelberg,2005.
[5] A. Frank and A. Asuncion. ”UCI machine learning repository”, 2010.
[6] M. A. Inc. Amazon Elastic Compute Cloud (Amazon EC2). Amazon Inc., http://aws.amazon.com/ec2/pricing, 2008.
[7] A. Bansal, T. Chen, and S. Zhong.” Privacy preserving backpropagation neural network learning over arbitrarily partitioned data”. Neural Comput. Appl., 20(1):143150, Feb. 2011.
[8] SumitGoyal, Gyanendra Kumar Goyal,”Radial Basis (Exact Fit) Artificial NeuralNetwork Technique for Estimating Shelf Life of Burfi”, Advances in Computer Scienceand its Applications (ISSN 2166-2924) 93 Vol. 1, No. 2, June 2012.
[9] SumitGoyal ,Gyanendra Kumar Goyal,”A Novel Method for Shelf Life Detection of Processed Cheese Using Cascade Single and Multi-Layer Artificial Neural Network Computing Models”, ARPN Journal of Systems and Software, VOL.2, NO.2,February 2012.
[10] Kaushik Deb, Ibrahim Khan, AnikSaha, Kang-Hyun Jo,”An Efficient Method ofVehicle License Plate Recognition Based on Sliding Concentric Windows and Artificial Neural Network”,2nd International Conference on Computer, Communication, Controla.nd Information Technology( C3IT-2012) on February 25 -26, 2012
[11] K.S. Kasiviswanathan, Avinash Agarwal,”Radio Basis Function Artificial Neural Network: Spread Selection”, International Journal of Advanced Computer Science,Vol.2,No.11, pp. 394-398, 2012.
[12] Yuan Jing, Minfang, Qi, Zhongguang, Fu, ”Prediction of coal calorific value based on the RBF neural network optimized by genetic algorithm”, Natural Computation (ICNC),2012 Eighth International Conference, pp. 440- 443, 2012.
[13] AnujaNagare, Shalini Bhatia, ”Traffic Flow Control using Neural Network”, International Journal of Applied Information Systems (IJAIS), Volume 1 No.2, January2012.
[14] PriyabrataKarmakar, Bappaditya Roy, Tirthankar Paul, Shreema Manna, ”Target Classification: An application of Artificial Neural Network in Intelligent Transport System”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 6, June2012.
[15] Feng Guo, Su-qin Zhang, Deng-bin Zhang, Wei Gao, ”Application of Genetic Neural Network on LifelessRepairable Spares Consumption Forecasting, Computer Science and Service System (CSSS)”, 2012 International Conference, pp.1313-1315, 2012.
[16] Paulraj M. P, MohdShuhanazZanarAzalan, Hema C.R., Rajkumar Palaniappan, ”Image Quality Assessment
-
[1] M. S.-liang, W. A.-li and Y. M.-Ji, “A Novel Image Denoising Method Based on Curvelet Transform”, [J]. JOURNAL HARBIN UNIV. SCI. & TECH. ,vol. 13, no. 2, (2008), pp. 78-81.
[2] D. L. Donoho, “De-noising by soft-thresholding, [J]. IEEE Trans on IT, (1995), vol. 41, no. 3, pp. 613-627.
[3] S. G. Chang, B. Yu and M. Vetterli, “Adaptive wavelet thresholding for image de-noising and compression”, [J].IEEE Trans. on Image Processing, vol. 9, no. 9, (2000), pp. 1532-1546.
[4] J. Portilla, V. Strela and M. J. Wainwright, “Image denoising using scale mixture of Gaussians in the wavelet domain”, [J]. IEEE Trans. on Image Processing, vol. 12, no. 11, (2003), pp. 1338-1351.
[5] M. S. Crouse, R. D. Nowak and R. G. Baraniuk, “Wavelet-based statistical signal processing using hidden Markov models “, [J]. IEEE Trans. on Image Processing, vol. 46, no. 4, (1998), pp. 886-902.
[6] L. Sendur, I. Selesnick and W. Bivariate, “Shrinkage functions for wavelet-based de-noising exploiting interscale dependency”, [J]. IEEE Trans. on Signal Processing,vol. 50, no. 11, (2002), pp. 2744-2756.
[7] M. N. Do, M. Vetterli, “Contour lets: A directional multiesolution image representation “,C]. International Conference on Image Processing, vol. 1, (2002), pp. 357- 360.
[8] M. N. Do and M Vetterli, “The Contour let transform: An efficient directional multiresolution image representation”, [J]. IEEE Trans Image Process,vol. 14, no. 12, (2005), pp. 2091-2106.
[9] D. Y. Duncan and M. N. Do, “Directional multiscale statistical modeling of images using the Contour let transform”, [J]. IEEE Trans Image Process,vol. 15, no. 6, (2006), pp. 1610-1620
[10] M. N. Do and M Vetterli, “Pyramidal directional filter banks and curvelets”, [C]. Proceedings of International Conference on Image Processing, (2001) October, pp. 158- 161.
[11] A. L. Cunha, J. Zhou and M. N. Do, “The non sub sampled Contour let transform: Theory desing and application”, [J]. IEEE Trans Image Processing, (2006), vol. 15, no. 10, pp. 3089-3101.
[12] N. Weyrich and G. T. Warhola, “Wavelet shrinkage and generalized cross validation for image de-noising”, [J]. IEEE Transactions on Image Proceeding, vol. 7, no. 1, (1998), pp. 82ï¼90.