Abstract
Multiwavelet analysis has been a powerful tool in image processing. But the theory is seldom applied to denoise cellular images of common phytoplankton. This paper proposes a new method which uses multiwavelets combining soft thresholding to meet this kind of applications. Three types of mutiwavelets thresholding are considered: scalar, decor and vector. Results of numerical experiments show that this method particularly using Chui-Lian orthonormal multiwavelet has better effectiveness than other methods referred in this paper for removing Gaussian noise.
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References
Strela, V., Walden, A.: Signal and Image Denoising Via Wavelet Thresholding: Orthogonal and Biorthogonal, Scalar and Multiple Wavelet Transforms. Imperial College Statistics Section, Technical Report TR-98-01 (1998)
Goodman, T.N.T., Lee, S.L., Tang, W.S.: Wavelet in Wandering Subspaces. Trans Amer Math Soc. 338, 639–654 (1993)
Chui, C.K., Lian, J.: A Study of Orthonormal Muti-Wavelets. Appl. Numer. Math. 20, 273–298 (1996)
Montefusco, L.B, Pap, S.: A Parameter Selection Method for Wavelet Shrinkage Denoising BIT Numerical Nathematics. Bit Numerical Mathematics 43, 611–626 (2003)
Shen, L., Tan, H.H., Tham, J.Y.: Symmetric-Antisymmetric Orthonormal Multiwavelets and Related Scalar Wavelets. IEEE Trans. Sign. Proc. 8, 258–279 (2000)
Donoho, D.L., Johnstone, I.: Adapting to Unknown Smoothness Via Wavelet Shrinkage. J Amer. Statist. Assoc. 90, 1200–1224 (1994)
Donoho, D., Johnstone, I.: Ideal Spatial Adaptation by Wavelet Shrinkage. Biometrika 81, 425–455 (1994)
Downie, T.R., Silverman, B.W.: The Discrete Multiple Wavelet Transform and Thresholding Methods. IEEE Trans Signal Processing 46, 2558–2561 (1998)
Downie, T.R.: Wavelet Metholds in Statistics. PhD thesis, University of Bristol (1997)
Silvia, B., Serena, P.: Matrix thresholding for Multiwavelet Image Denoising. Numerical Algorithms 33, 41–51 (2003)
Strela, V., Walden, A.T.: Orthogonal and Biorthogonal Multiwavelets for Signal Denoising and Image Compression. Proc SPIE 3391, 96–107 (1998)
Donovan, G., Geronimo, J.S., Hardin, D.P, Massopust, P.R.: Construction of Orthogonal Wavelets Using Fractal Interpolation Functions. SIAM J Math Anal. 27, 1158–1192 (1996)
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Ji, G., Wang, N., Wang, Y., Xu, L. (2007). Multiwavelet Denoising for Common Phytoplankton Cellular Images. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_100
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DOI: https://doi.org/10.1007/978-3-540-74282-1_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74281-4
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