A Comparative Study between X_Lets Family for Image Denoising
Автор: Beladgham Mohamed, Habchi Yassine, Moulay Lakhdar Abdelmouneim, Abdesselam Bassou, Taleb-Ahmed Abdelmalik
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 3 vol.6, 2014 года.
Бесплатный доступ
Research good representation is a problem in image processing for this, our works are focused in developing and proposes some new transform which can represent the edge of image more efficiently, Among these transform we find the wavelet and ridgelet transform these both types transforms are not optimal for images with complex geometry, so we replace this two types classical transform with other effectiveness transform named bandelet transform, this transform is appropriate for the analysis of edges of the images and can preserve the detail information of high frequency of noisy image. De-noising is one of the most interesting and widely investigated topics in image processing area. In order to eliminate noise we exploit in this paper the geometrical advantages offered by the bandelet transform to solve the problem of image de-noising. To arrive to determine which type transform allows us high quality visual image, a comparison is made between bandelet, curvelet, ridgelet and wavelet transform, after determining the best transform, we going to determine which type of image is adapted to this transform. Numerically, we show that bandelet transform can significantly outperform and gives good performances for medical image type TOREX, and this is justified by a higher PSNR value for gray images.
Bandelet transform, Contourlet transform, Curvelet transform, Ridgelet transform, Quadtree segmentation
Короткий адрес: https://sciup.org/15013273
IDR: 15013273
Список литературы A Comparative Study between X_Lets Family for Image Denoising
- B. Schiener, "Applied Cryptography", John Wiley & Sons, 1996.
- A. K. Jain, A. Ross, and S. Pankanti, "Biometrics: A Tool for Information Security," IEEE Trans on Information Forensics and Security, vol. 1, no. 2, pp. 125-143, 2006.
- A.K. Jain, A. Ross, S. Prabhakar, “An Introduction to biometric recognition”, IEEE Transactions on Circuits and Systems for Video Technology, 14(1):4-20, 2004.
- A. Ross, K. Nandkumar, and A. K. Jain. "Handbook of Multibiometrics", Springer Verlag, 2006.
- J. Daugman, "Probing the uniqueness and randomness of Iris Codes: Results from 200 billion iris pair comparisons," Proc. IEEE, vol. 94, no. 11, pp. 1927-1935, 2006.
- J. Bhatnagar, and A. Kumar, "On Estimating Some Performance Indices for Biometric Identification," Pattern Recognition, vol. 42, no. 5, pp. 1805-1818, 2009.
- Y. Adini, Y. Moses, S. Ullman, “Face recognition: the problem of compensating for changes in illumination direction”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):721-732, 1997.
- R G. Gallager, "A Simple Derivation of the Coding Theorem and Some Applications," IEEE Trans on Info. Theory, vol. 11, No. 1, 1965, pp. 3-18.
- R. Duda, P. Hart, and D. Stork, "Pattern Classification", Wiley student edition, 1997.
- J. Ortega-Garcia, J. Bigun, D. Reynolds, J. Gonzalez-Rodriguez, “Authentication gets personal with biometrics”, IEEE Signal Processing Magazine, 21(2):50-62, 2004.
- M. Farenzena, L. Bazzani, A. Perina, V. Murino, and M. Cristani. “Person re-identi?cation by symmetry-driven accumulation of local features”, In Computer Vision and Pattern Recognition, pages 2360–2367, 2010.
- D. Gorodnichy and R. Hoshino “Calibrated con?dence scoring for biometric identi?cation”, In Proceedings of NIST International Biometric Performance Conference, 2010.
- S C. Dass, Y. Zhu, and A. K. Jain, "Validating a Biometric Authentication: Sample size requirements," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 1902 - 1919, 2006.
- N.K. Ratha, J.H. Connell, R.M. Bolle, “Enhancing security and privacy in biometrics-based authentication systems” IBM Systems Journal, 40(3):614-634, 2001.
- R. Ryan: The importance of biometric standards. Biometric Technology Today, 17(7):7-10, 2009.
- F. Deravi: Biometrics standards. Advances in biometrics, 473-489, 2008.
- J. Bhatnagar, and A. Kumar, "On Some Performance Measures for Biometric Identification," Proc. of IEEE ICB, 2007, pp. 1035 - 1048.
- Y. Zhu, S.C. Dass, and A.K Jain, "Statistical Models for Assessing Individuality of Fingerprints," EEE Trans. on Information Forensics and Security, vol. 2, no. 3, pp. 391-401, 2007.
- J. Bhatnagar, A. Kumar, and N. Saggar, "A Novel Approach to Improve Biometric Recognition Using Rank Level Fusion," Proc. of IEEE CVPR, 2007, pp. 43-51.
- Jain, A. K., Flynn, P. J. & Ross, A. eds., 2007, “Handbook of biometrics”, Springer.
- Jain, A. K., Nandakumar, K., & Nagar, A., 2008, “Biometric Template Security”, EURASIP Journal on Advances in Signal Processing, vol. 2008, Article ID 579416.
- Li, S. Z. & Jain, A. K. eds., 2005, “Handbook of face recognition”, Springer.
- Prabhakar, S. Pankanti, S., & Jain, A. K., 2003, “Biometric recognition: security & privacy concerns”, IEEE Security & Privacy Magazine, 1(2), pp. 33-42.
- Ross, A., Nandakumar, K., & Jain, A.K., 2006, “Handbook of multibiometrics”, Springer.
- J. Bhatnagar, and A. Kumar, "Estimating Minimum Sample Size Requirements for Reliable Identification," Proc. of IEEE CVPR, 2006, pp. 18- 25.
- Rowe, R. K., 2005, “A multispectral sensor for fingerprint spoof detection Sensors”, 22(1), pp. 1-4.
- Wayman, J., Jain, A. K., Maltoni, D., & Maio, D. eds., 2005, “Biometric systems: technology, design and performance evaluation”, Springer.
