A New Hybridization of Bilateral and Wavelet Filters for Noisy De-Noisy Images

Authors: Wasfi T. Saalih Kahwachi1 & Hawkar Q. Birdawod2
1Research Center Director, Tishk International University, Erbil, Iraq
2Department of Business Administration, College of Administration and Financial Science, Cihan University, Erbil, Iraq

Abstract: In this work we propose, a hybrid noise reduction algorithm that is a combination of a spatial field binary filter and a hybrid wave field threshold function. These two methods are used to stop Gaussian noise. The hybrid filter is a nonlinear filter that deals with spatial averaging of non-uniform edges. We found it to be an effective technique for image reduction. Determining filter parameters for the mixed filter is important to avoid large differences in results, besides the issue of acceleration velocity. This hybrid model, binary filtering, and Wavelet Thresholding have tried standard images, such as normal eyes, MRI, Roya Face, Ultrasound, X-Ray, and Rawa. Different Gaussian noise was added with different standard deviations σ = 10, 20, 35, 40, and 50. The peak-to-noise ratio (PSNR) signal, MSE, VIF, IQI, and the proposed model MSE between pixels were used as quantitative measures of performance of the relative noise reduction algorithms and then were compared to the models.

Keywords: Image Denoising, Wavelet Transform, Wavelet Thresholding, Bilateral Filter

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Doi: 10.23918/eajse.v9i1p99

Published: January 15, 2023

References

Al-Talib, M. S. (2019). Approximate estimator for parameters of non-normal VMA (1) model. IRAQI JOURNAL OF STATISTICAL SCIENCES, 164147.

Bao, P. a. (2003). Noise reduction for magnetic resonance images via adaptive multiscale products thresholding. IEEE transactions on medical imaging, 1089-1099.

Blbas, H. &. (2021). A Comparison Between New Modification of ANWK and Classical ANWK Methods in Nonparametric Regression. Cihan University-Erbil Scientific Journal, 32-37.

Buades, A. C. (2006). Neighborhood filters and PDE’s. Numerische Mathematik, 1-34.

Chambolle, A. D. (1998). Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage. IEEE Transactions on Image Processing, 319-335.

Chang, S. Y. (1998). Spatially adaptive wavelet thresholding with context modeling for image denoising. ICIP Transactions on Image Processing, 535-539.

Chang, S. Y. (2000). Adaptive wavelet thresholding for image denoising and compression. IEEE transactions on image processing, 1532-1546.

Chang, S. Y. (2000). Spatially adaptive wavelet thresholding with context modeling for image denoising. IEEE Transactions on Image Processing, 1522-1531.

Daubechies, I. (1990). The wavelet transforms, time-frequency localization and signal analysis. IEEE transactions on information theory, 961-1005.

Donoho, D. (1995). De-noising by soft-thresholding. IEEE transactions on information theory, 613-627.

Donoho, D. a. (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika, 425-455.

Donoho, D. a. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of the American statistical association, 1200-1224.

Donoho, D. J.-3. (1995). Wavelet shrinkage: Asymptopia? . Journal of the Royal Statistical Society. Series B , 301-369.

Fan, G. a. (2001). Image denoising using a local contextual hidden Markov model in the wavelet domain. IEEE Signal Processing Letters, 125-128.

Guo, H. O. (1994). Wavelet based speckle reduction with application to SAR based ATD/R. In Image Processing. Proceedings. ICIP-94., IEEE International Conference, 75-79.

Hu, H. L. (2014). Removing Mixture of Gaussian and Impulse Noise by Patch-Based Weighted Means. Preprint submitted to Elsevier arXiv: 1403.2482v1 [cs.CV]. 67(1), 1-29.

Kachouie, N. (2009). Image Denoising Using Earth Mover’s Distance and Local Histograms. International Journal of Image Processing, 66.

Kahwachi, W. (2005). HIGH RESOLUTION IMAGE CLASSIFICATION. IRAQI JOURNAL OF STATISTICAL SCIENCES, 15-21.

Kahwachi, W. a. (2006). Automatic Fingerprint Identification System Using Robust Distance. TANMIYAT AL-RAFIDAIN, 27-38.

Liu, J. a. (2001). Information-theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients. IEEE Transactions on Image Processing, 1647-1658.

Mallat, S. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE transactions on pattern analysis and machine intelligence, 674-693.

Misiti, M. M. (2007). Wavelets and their Applications. John Wiley & Sons. Published by ISTE UK.

Misiti, M. M. (2013). Wavelets and their Applications. John Wiley & Sons.

Motwani, M. G. (2004). Survey of image denoising techniques. GSPX (pp. 27-30). In Proceedings of GSPX .

Nasersharif, B. a. (2004). Application of wavelet transform and wavelet thresholding in robust sub-band speech recognition. In Signal Processing Conference, 2004 12th European (pp. 345-348). IEEE.

Nowak, R. (1999). Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Transactions on Image Processing, 1408-1419.

Omer, F. M. (2020). Forecasting the Beef Meat Prices in Erbil Using Box-Jenkins Models. Eurasian Journal of Management & Social Sciences, 1-16.

Perona, P. a. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 629-639.

Roy, S. S. (2010). A new hybrid image denoising method. International Journal of Information Technology and Knowledge Management, 491-497.

Rudin, L. O. (1992). Nonlinear total variation-based noise removal algorithms. Physica D: Nonlinear Phenomena, 259-268.

Saeed, H. K.-T. (2017). The Bayesian Estimate of Vector Autoregressive Model Parameters Adopt Informative prior- Information. AL-Anbar University journal of Economic and Administration Sciences, 273-286.

Tomasi, C. a. (1998). Bilateral filtering for gray and color images. IEEE, 839-846.

Wang, Z. a. (2002). A universal image quality index. IEEE signal processing letters, 81-84.

Zhang, M. a. (2008). A new image denoising method based on the bilateral filter. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE., 929-932.

Zhang, M. a. (2008). Multiresolution bilateral filtering for image denoising. IEEE Transactions on Image Processing, 2324-2333.

Zhang, M. a. (2008). Multiresolution bilateral filtering for image denoising. . IEEE Transactions on Image Processing, 2324-2333.