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Stationary wavelet transform : ウィキペディア英語版 | Stationary wavelet transform
The Stationary wavelet transform (SWT)〔James E. Fowler: (The Redundant Discrete Wavelet Transform and Additive Noise ), contains an overview of different names for this transform.〕 is a wavelet transform algorithm designed to overcome the lack of translation-invariance of the discrete wavelet transform (DWT). Translation-invariance is achieved by removing the downsamplers and upsamplers in the DWT and upsampling the filter coefficients by a factor of in the th level of the algorithm.〔A.N. Akansu and Y. Liu, On Signal Decomposition Techniques, Optical Engineering, pp. 912-920, July 1991.〕〔M.J. Shensa, The Discrete Wavelet Transform: Wedding the A Trous and Mallat Algorithms, IEEE Transaction on Signal Processing, Vol 40, No 10, Oct. 1992.〕〔M.V. Tazebay and A.N. Akansu, Progressive Optimality in Hierarchical Filter Banks, Proc. IEEE International Conference on Image Processing (ICIP), Vol 1, pp. 825-829, Nov. 1994.〕〔M.V. Tazebay and A.N. Akansu, Adaptive Subband Transforms in Time-Frequency Excisers for DSSS Communications Systems , IEEE Transaction on Signal Processing, Vol 43, No 11, pp. 2776-2782, Nov. 1995.〕 The SWT is an inherently redundant scheme as the output of each level of SWT contains the same number of samples as the input – so for a decomposition of N levels there is a redundancy of N in the wavelet coefficients. This algorithm is more famously known as "''algorithme à trous''" in French (word ''trous'' means holes in English) which refers to inserting zeros in the filters. It was introduced by Holschneider et al.〔M. Holschneider, R. Kronland-Martinet, J. Morlet and P. Tchamitchian. A real-time algorithm for signal analysis with the help of the wavelet transform. In ''Wavelets, Time-Frequency Methods and Phase Space'', pp. 289–297. Springer-Verlag, 1989.〕 ==Implementation== The following block diagram depicts the digital implementation of SWT. In the above diagram, filters in each level are up-sampled versions of the previous (see figure below).
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