Image De-noising using 2-D Circular-Support Wavelet Transform
Images are often suffering from two main corruptions (unwanted modifications). These modifications in image accuracy are categorized as blur and noise. Noise appears during different image processing phases of acquisition, transmission, and retrieval. The purpose of any de-noising algorithm is to remove such noise while maintaining as much as possible image details. A 2-D circular-support wavelet transform (2-D CSWT) is anticipated in this paper to be utilized as an image de-noising algorithm. The realization of such de-noising algorithm is accomplished in the form of some competent mask filters. De-noising by thresholding processes can be applied on all 2-D high-pass coefficient channels with different thresholding levels. Lena noisy image with different levels of noise (Salt and Pepper, and Gaussian) has been used to assess the performance of such de-noising scheme. Test are done in terms of PSNR and correlation factor of the reconstructed image. A comparative study between the Conventional wavelet transform and the 2-D CSWT done in this paper.
B. Ergen, “Signal and Image Denoising Using Wavelet Transform”, Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology, A book edited under CC BY 3.0 license by Dumitru Baleanu, ISBN 978-953-51-0494-0, April 2012.
C. Xiong, J. Tian, and J. Liu, “Efficient Architectures for Two-Dimensional Discrete Wavelet Transform using Lifting Scheme", IEEE Transactions on Image Processing, Vol. 16, No. 3, pp. 607 – 614, March 2007.
F. Vatansever, F. Uysal and A. Uzun, “Ayrik Dalgacik Dönüşümü İle Gürültü Süzme”, 2007, www.emo.org.tr/ekler/7841cc9e552bd5c_ek.pdf.
M. İkiz, M. Akin, B.Kurt and H. Acar, “Recognation The Speaker Identy By Means Of Wavelet Analysis And Neural Network”, Doğu Anadolu Bölgesi Araştırmaları, 2007.
B. Demir and S. Ertürk, “Wavelet Denoising Before Support Vector Classification of Hyperspectral Images”, SIU 2007- IEEE 15th Conference on Signal Processing and Communications Applications, 1-13 June 2007, Eskisehir, Turkey, 2007.
M. K. Singh, “Denoising of Natural Images Using the Wavelet Transform”, M. Sc. Paper submitted to The Faculty of the Department of Electrical Engineering, San Jos´e State University, Dec. 2010.
B. Ergen and M. Baykara, “Analysis of De-noising with Wavelet and Wavelet Package Decomposition”, E-Journal of New World Sciences Academy, Vol. 6, No. 2, pp. 518-526, 2011.
M. Üstündağ, E. Avci, M. Gökbulut and Fikret Ata, “Denoising of Weak Radar Signals using Wavelet Packet Transform and Genetic Algorithm”, Journal of the Faculty of Engineering and Architecture of Gazi University, Vol. 29, No. 2, pp. 375-383, 2014.
A. Srivastava and S. Maheshwari, “Signal Denoising and Multiresolution Analysis by Discrete Wavelet Transform”, Innovative Trends in Applied Physical, Chemical, Mathematical Sciences and Emerging Energy Technology for Sustainable Development, 2015. www.krishisa nskriti.org/vol_image/23Sep201506092619.pdf.
M. Mastriani, “Quantum Boolean image denoising”, Quantum Inf. Process, Vol. 14, pp. 1647–1673, 2015.
J. M. Abdul-Jabbar, Z. T. Abede and A. A. Dawood, “A Multiplier-less Implementation of Two-Dimensional Circular-Support Wavelet Transform on FPGA”, Iraq J. Electrical and Electronic Engineering, Vol.9, No.1, pp. 16-28, 2013.
J. M. Abdul-Jabbar and Z. N. Abdulkader, ” Iris Recognition using 2-D Elliptical–Support Wavelet Filter Bank”, International Conference on Image Processing Theory, Tool and Applications- IPTA 2012, Istanbul, Turkey, 15-18 Oct., pp. 359 – 363, 2012.
J. M. Abdul-Jabbar and H. N. Fathee, “Design and Realization of Circular Contourlet Transform”, Al-Rafidain Engineering Journal, Vol. 18, No. 4, pp. 28 - 42, Aug. 2010.
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