Image denoising using new wavelet thresholding function

• Autorzy: Dehda B. , Melkemi K.

Identyfikatory

DOI

10.17512/jamcm.2017.2.05

• Języki publikacji: EN

Abstrakty

EN

In this paper, we propose a new image denoising method based on wavelet thresholding. In this method, we introduce a new nonlinear thresholding function characterized by a shape parameter and basic properties. These characteristics make the new method able to achieve a compromise between both traditional thresholding techniques such as Hard and Soft thresholding. The experimental results show that our proposed method provides better performance compared to many classical thresholding methods in terms of the visual quality of the denoised image.

Słowa kluczowe

EN

wavelet transform; image denoising; wavelet thresholding; wavelet shrinkage rules; peak signal to noise ratio; PSNR

PL

transformata falkowa; falka; odszumianie obrazu

• Wydawca: Wydawnictwo Politechniki Częstochowskiej
• Czasopismo: Journal of Applied Mathematics and Computational Mechanics
• Rocznik: 2017
• Tom: Vol. 16, nr 2
• Strony: 55--65
• Opis fizyczny: Bibliogr. 13 poz., rys., tab.

Twórcy

autor

Dehda B.

• Department of Mathematics, University of Mohamed Khider Biskra, Algeria

autor

Melkemi K.

• Department of Mathematics, University of Elhadj Lakhdar Batna, Algeria

Bibliografia

• [1] Rajni, Anutam, Image denoising techniques - an overview, International Journal of Computer Applications (0975-8887) 2014, 86, 16, January, 13-17.
• [2] Anbarjafari G., Demirel H., Gokus A.E., A novel multi-diagonal matrix filter for binary image denoising, J. Adv. Electr. Comput. Eng. 2014, 1(1), 14-21.
• [3] Li L., Ge H., Zhang Y., Low-density noise removal based on lambda multi-diagonal matrix filter for binary image, Neural Comput & Applic. 2016, DOI:10.1007/s00521-016-2538-7.
• [4] Dehda B., Melkemi K., Novel method for reduction of wavelet coefficients number and its applications in images compression, IJAMML 2016, 5, 1, 43-65.
• [5] Meyer Y., Wavelets and Operators, Cambridge Univ. Press, Cambridge 1992.
• [6] En-Bing Lin, Yi Ling, Image compression and denoising via nonseparable wavelet approximation, Journal of Computational and Applied Mathematics 2003, 155, 131-152.
• [7] Chang S.G. et al., Adaptive wavelet thresholding for image denoising and compression, IEEE Transactions on Image Processing 2000, 9, 9, September.
• [8] Donoho D.L., Johnstone I.M., Adapting to unknown smoothness via wavelet shrinkage, J. Am. Statist. Assoc. 1995, 90, 1200-1224.
• [9] Zhang X.-P. et al., Adaptive denoising based on sure risk, IEEE Signal Proessing Letters 1998, 5, 10, October.
• [10] Tang J.-Y., Chen W.-T., Chen S.-Y., Zhou W., Wavelet-based vibration signal denoising with a new adaptive thresholding function, Journal of Vibration and Shock 2009, 28, 7, 118-121.
• [11] Gao H.Y., Wavelet shrinkage denoising using the non-negative garrote, J. Comput. Graph. Statist. 1998, 7, 469-488.
• [12] Kushner H.J., Yin G.G., Stochastic Approximation Algorithms and Applications, Application of Mathematics 1997, Vol. 35, Springer, 1997, DOI:10.1007/978-1-4899-2696-8. ISBN 978-1-4899-2698-2.
• [13] Rahman T., Jianqiang Gao, Srinivas Nowduri, Segmentation of MRI images via mathematical morphology, IJAMML 2016, 5, 2, 115-121.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).