[1] Q. Zhao, X. Wang, B. Ye, and D.Zhou, “ Mixed image denoisingmethod of non-local means and adaptive bayesian threshold estimationin NSCT domain”, 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), Vol.6, pp. 636-639, 9-11 July 2010.
[2] B.Li,X.Li,S.Wang,andH.Li;,“A Multiscale and Multidirectional Image Denoising Algorithm Based onContourlet Transform”, International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP ’06).pp. 635-638, Dec. 2006.
[3] R. Sivakumar, G.Balaji, R. Ravikiran, R. Karikalan, and S. Janaki,“ Image Denoising using ContourletTransform”, Second International Conference on Computer and Electrical Engineering, (ICCEE ’09). Vol.1, pp. 22-25, 28-30 Dec. 2009.
[4] Po, D.D.-Y, and Do, M.N,“ Directional multi-scale modeling of images using the contourlet transform”. IEEE Transactions on Image Processing, Vol.15, No.6, pp. 1610-1620, June 2006.
[5] B. Matalon, M. Elad, and M. Zibulevsky, “Image denoising with the contourlet transform”. In Proceedings of SPARSE’05, Rennes, France, Nov. 2005.
[6] A. Altun, and N. Allahverd, “Recognition of fingerprints enhanced by contourlet transform with artificial neural networks”, 28th International Conference on Information Technology Interfaces (ITI), pp. 167-172, Croatia, 19-22 June 2006.
[7] Y. Yan, and L. Osadciw, “ContourletBased Image Recovery and De-noisingThrough Wireless Fading Channels”,Conference on Information Science and Systems, The Johns Hopkins University, 16-18 March 2005.
[8] R. Eslami, and H. Radha, “ Image denoising using translation-invariant contourlet transform”, IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings. (ICASSP ’05). Vol.4, pp. 557- 560, 18-23 March 2005.
[9] R. Eslami, and H. Radha, “ Optimal Linear Combination of Denoising Schemes for Efficient Removal of Image Artifacts”, IEEE International Confere-nce on Multimedia and Expo, pp. 465-468, 9-12 July 2006.
[10] B. Song, L. Xu, and W. Sun, “ImageDenoising Using Hybrid Contourlet and Bandelet Transforms”, Fourth Internati-onal Conference on Image and Graphics (ICIG). pp. 71-74, 22-24 Aug. 2007.
[11] Z. Liu, and H. Xu, “Image Denoising with Nonsubsampled Wavelet-Based Contourlet Transform”, Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD’08). pp. 301-305, 18-20 Oct. 2008.
[12] Q. Xiaobo, and Y. Jingwen, “ The cycle spinning-based sharp frequency localized contourlet transform forimage denoising”, 3rd International Conference on Intelligent System and Knowledge Engineering (ISKE) , pp. 1247-1251, 17-19 Nov. 2008.
[13] K. Li,W. Wang, and J. Gao, “ ImageDenoising Based on Contourlet- Domain HMT Models Using Cycle Spinning”, 2008 International Conference on Computer Science and Software Engineering, pp.162-165, 12- 14 Dec. 2008.
[14] Z. Zhou, J. Cao, and W. Liu,“Contourlet-based image denoising algorithm using adaptive windows”, 4thIEEE Conference on Industrial Electro- nics and Applications (ICIEA).pp. 3654-3657, 25-27 May 2009.
[15] X. Wu, B. Guo, S. Qu, and Z. Wang,“A New Adaptive Image Denoising Method Combining the Nonsubsampled Contourlet Transform and Total Variation”, Fifth International Conference on Information Assurance and Security (IAS ’09). pp. 581-584, 18-20 Aug. 2009.
[16] C. Lu, J. Shen, and X. Yu, “ Bottleimage de-noising using adaptive threshold based on nonsubsampledcontourlet transform”, 2nd International Conference on Signal Processing Systems (ICSPS), pp. 792-795, 5-7 July 2010.
[17] Z. Liu, and H. Xu, “Image denoisingusing Contourlet and two-dimensional Principle Component Analysis”,International Conference on Image Analysis and Signal Processing (IASP), pp. 309- 313, 9-11 April 2010.
[18] N.Sugitha, S. Arivazhagan, “Performance analysis of multiresolution image denoising schemes”, IEEE InternationalConference on Communi- cation Control and Computing Technologies (ICCCCT), pp. 413-418, 7-9 Oct. 2010.
[19] Ch. Srinivasa rao , S. Srinivas kumarand B.N.Chatterji, “ Content BasedImage Retrieval using ContourletTransform”, ICGST-GVIP Journal, Vol. 7, pp. 9- 15, Nov. 2007.
[20] M. Do, M.Vetterli, “The contourlet transform: an efficient directionalmultiresolution image representation”,IEEE Transactions on Image Processing, Vol.14, No.12, pp. 2091- 2106, Dec. 2005.
[21] Y. Tanaka, M. Ikehara, and T. Nguyen,“ Multiresolution Image Repres-entation Using Combined 2-D and 1-D Directional Filter Banks”,IEEE Transactions on Image Processing, Vol.18, No.2, pp. 269-280, Feb. 2009.
Tikrit Journal of Engineering Sciences (2013) 20(4) 11-22
A Contourlet-Based Image Denoising Technique with Coefficient Threshold Level Estimation
Akram A. Dawood
Computer Eng. Dept., Mosul University, Iraq
Abstract
In this paper, a new image denoising technique is proposed based on contourlet transform. Many random images are generated simulating the standard deviation level of the original noisy image and the contourlet threshold level is then calculated based on such simulations. Different contourlet coefficients are thresholded by such precalculated contourlet thresholds indicating a nonlinear thresholding manner. The resulting denoised images posses the superiority of the proposed technique over three other recents. Subjective and objective measurements of the proposed technique support such superiority.
Keywords: Contourlet Transform, Laplacian Pyramid, Directional Filter Banks and Random Images.
How to cite
TJES: Dawood AA. A Contourlet-Based Image Denoising Technique with Coefficient Threshold Level Estimation. Tikrit Journal of Engineering Sciences 2013; 20(4): 11-22.
APA: Dawood, A. A.(2013). A Contourlet-Based Image Denoising Technique with Coefficient Threshold Level Estimation.Tikrit Journal of Engineering Sciences, 20(4), 11-22.
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