Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
In this paper a new method for adaptive synthesis of a smooth orthogonal wavelet, using fast neural network and genetic algorithm, is introduced. Orthogonal lattice structure is presented. A new method of supervised training of fast neural network is introduced to synthesize a wavelet with desired energy distribution between output signals from low–pass and high–pass filters on subsequent levels of a Discrete Wavelet Transform. Genetic algorithm is proposed as a global optimization method for defined objective function, while neural network is used as a local optimization method to further improve the result. Proposed approach is tested by synthesizing wavelets with expected energy distribution between low– and high–pass filters. Energy compaction of proposed method and Daubechies wavelets is compared. Tests are performed using image signals.
Czasopismo
Rocznik
Tom
Strony
417--433
Opis fizyczny
Bibliogr. 26 poz., rys., wzory
Twórcy
autor
- Institute of Information Technology, Technical University of Lodz, Wolczanska str. 215, 93-005 Lodz, Poland, jan.stolarek@p.lodz.pl.
Bibliografia
- [1] H.-G. BEYER and H.-P. SCHWEFEL: Evolution strategies - a comprehensive introduction. Natural Computing, 1(1), (2002), 3-52.
- [2] T. COOKLEV: An efficient architecture for orthogonal wavelet transforms. IDEE Signal Processing Letters, 13(2), (2006).
- [3] I. DAUBECHIES: Ten Lectures on Wavelets. SIAM, 1992.
- [4] W. DIETL, P. MEERWALD and A. UHL: Protection of wavelet-based watermarking systems using filter parametrization. Signal Processing, 83(10), (2003), 2095-2116.
- [5] J. J. HU and E. D. GOODMAN: The Hierarchical Fair Competition (HFC) model for parallel evolutionary algorithms. In 2002 Congress on Evolutionary Computation, (2002).
- [6] Z. KOWALCZUK and T. BIAŁASZEWSKI: Genetic algorithms in multi-objective optimization of detection observers, Springer-Verlag, 2004, 511-556.
- [7] M. LANG and P. N. HELLER: The design of maximally smooth wavelets. In Proc. of the Acoustics, Speech, and Signal Processing, 3 (1996), 1463-1466.
- [8] P. LIPINSKI and M. YATSYMIRSKYY: On synthesis of 4-tap and 6-tap reversible wavelet filters. Przeglad Elektrotechniczny, 12 (2008), 284-286.
- [9] S. MALLAT: A wavelet tour of signal processing. Academic Press, December 2008.
- [10] P. MEERWALD and A. UHL: Watermark security via wavelet filter parametrization. In Int. Conf. on Image Processing, 3 (2001), 1027-1030.
- [11] J. E. ODEGARD and C. S. BURRUS: New class of wavelets for signal approximation. In IEEE Int. Symp. on Circuits and Systems (ISCAS), (1996).
- [12] S. OSOWSKI: Signal flow graphs and neural networks. Biological Cybernetics, 70(4), (1994), 387-395.
- [13] S. OSOWSKI and A. CICHOCKI: Application of SFG in learning algorithms of neural networks. In Int. Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, (1996), 75-83.
- [14] G. REGENSBURGER: Parametrizing compactly supported orthonormal wavelets by discrete moments. Applicable Algebra in Engineering, Communication and Computing, 18(6), (2007), 583-601.
- [15] P. RIEDER, J. GOTZE, J. S. NOSSEK and C. S. BURRUS: Parameterization of orthogonal wavelet transforms and their implementation. IEEE Trans. on Circuits and Systems II: Analog and Digital Signal Processing, 45(2), (1998), 217-226.
- [16] L.-K. SHARK and C. YU: Design of optimal shift–invariant orthonormal wavelet filter banks via genetic algorithm. IEEE Trans. on Signal Processing, 83 (2003), 2579-2591.
- [17] B. STASIAK and M. YATSYMIRSKYY: Fast orthogonal neural networks. Lecture Notes in Computer Science, 4029 (2006), 142-149.
- [18] J. STOLAREK: Synthesis of a wavelet transform using neural network. In XI Int. PhD Workshop OWD, Conference Archives PTETiS, 26 (2009).
- [19] J. STOLAREK: On the properties of a lattice structure for a wavelet filter bank implementation: Part 1. J. of Applied Computer Science, 19(1), (2011), to appear.
- [20] J. STOLAREK and P. LIPINSKI: Improving digital watermarking fidelity using Fast neural network for adaptive wavelet synthesis. J. of Applied Computer Science, 18(1), (2010), 61-74.
- [21] J. STOLAREK and M. YATSYMIRSKYY: Fast neural network for synthesis and implementation of orthogonal wavelet transform. In Image Processing & Communications Challenges. AOWEXIT, 2009.
- [22] W. SWELDENS: The lifting scheme: A new philosophy in biorthogonal wavelet constructions. In Wavelet Applications in Signal and Image Processing III, (1995), 68-79.
- [23] P. P. VAIDYANATHAN and P.-Q. HOANG: Lattice structures for optimal design and robust implementation of two-channel perfect-reconstruction QMF banks. IDEE Trans. on Acoustics, Speech and Signal Processing, 36(1), (1988), 81-94.
- [24] D. WEI, A. C. BOVIK and B. L. EVANS: Generalized coiflets: a new family of orthonormal wavelets. In Record of the Thirty-First Asilomar Conf. on Signals, Systems & Computers, 2 (1997), 1259-1263.
- [25] M. YATSYMIRSKYY: Lattice structures for synthesis and implementation of wavelet transforms. J. of Applied Computer Science, 17(1), (2009), 133-141.
- [26] H. ZOU and A. H. TEWFIK: Parametrization of compactly supported orthonormal wavelets. IEEE Trans. on Signal Processing, 41(3), (1993), 1428-1431.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW3-0073-0017