PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Image filtering with fast parametrized biorthogonal transforms implemented on a new GUI research aid system

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper the authors show that fast parametrized biorthogonal transforms (FPBT) are well suited for adaptive generalized Wiener image filtering. Research results are obtained with a use of a new graphical user interface system for implementing various fast adaptive techniques, designed, implemented and published by the authors as a part of a project Innovative Economy Programme 2007-2013 „Platforma Informatyczna TEWI”.
Rocznik
Strony
97--115
Opis fizyczny
Bibliogr. 20 poz.
Twórcy
autor
  • Institute of Information Technology Łódź University of Technology Wólczańska 215, 90-924 Łódź
autor
  • Institute of Information Technology Łódź University of Technology Wólczańska 215, 90-924 Łódź
  • Institute of Information Technology Łódź University of Technology Wólczańska 215, 90-924 Łódź
  • Institute of Information Technology Łódź University of Technology Wólczańska 215, 90-924 Łódź
Bibliografia
  • [1] Yatsymirskyy, M., A Novel Matrix Model of Two Channel Biorthogonal Filter Banks, Metody Informatyki Stosowanej, 2011, pp. 205–212, (in Polish).
  • [2] Yatsymirskyy, M. and Puchala, D., Fast Parametrized Biorthogonal Transforms, Przegląd Elektrotechniczny, Vol. 4, 2012, pp. 123–125.
  • [3] Ahmed, N. and Rao, K. R., Orthogonal Tranforms for Digital Signal Processing, Springer-Verlag, Berlin, Heidelberg, New York, 1975.
  • [4] Strang, G. and Nguyen, T., Wavelets and filter banks, Wellesley-Cambridge Press, 1999.
  • [5] Jayant, N. S. and Noll, P., Digital Coding of Waveforms, Prentice Hall, Engelwood Cliffs, NJ, 1984.
  • [6] Yatsymirskyy, M. and Puchala, D., Fast Neural Networks Learning Techniques for Signal Compression, Przegląd Elektrotechniczny, Vol. 1, 2010, pp. 189–191.
  • [7] Stokfiszewski, K. and Szczepaniak, P. S., An Adaptive Fast Transform Based Image Compression, Artificial Intelligence and Soft Computing - ICAISC, Lecture Notes in Computer Science, Vol. 5097, 2008, pp. 874–88.
  • [8] Zieliński, T., From theory to digital signal processing, WKŁ, 2009, (in Polish).
  • [9] Stasiak, B., Two-dimensional Fast Orthogonal Neural Network for Image Recognition, Lecture Notes in Computer Science, 2009, pp. 653–660.
  • [10] Stasiak, B. and Yatsymirskyy, M., On Feature Extraction Capabilities of Fast Orthogonal Neural Networks, Lecture Notes in Computer Science, 2007, pp. 27–36.
  • [11] Lipiński, P., Watermarking software in practical applications, Bulletin of the Polish Academy of Sciences: Technical Sciences, Vol. 59, No. 1, 2011, pp. 21–25.
  • [12] Pratt, W. K., Generalized Wiener Filtering Computation Techniques, IEEE Transactions on Computers, Vol. C-21, No. 7, 1972, pp. 636–641.
  • [13] Digital compression and coding of continuous-tone still images, Tech. Rep. T.81, International Telecommunication Union, 1992.
  • [14] Diamantaras, K. I. and Strintzis, M. G., Optimal Transform Coding in the Presence of Quantization Noise, IEEE Transactions on Image Processing, Vol. 8, No. 11, 1999, pp. 1508–1515.
  • [15] Yatsymirskyy, M. and Stasiak, B., Fast orthogonal neural networks for 2D signal processing, Przegla˛d Elektrotechniczny, Vol. 2, 2007, pp. 167–170.
  • [16] Yatsymirskyy, M. and Wiechno, T., Two-stage Fast Fourier and Hartley Transform of Real Data Sequence, Przegla˛d Elektrotechniczny, Vol. R. 86, No. 1, 2010, pp. 41–43.
  • [17] Yatsymirskyy, M. and Puchala, D., Fast Time-Decimated Algorithms of Discrete Two-Dimensional Cosine and Sine Transforms of Type II and type IV, Modelling and Information Technologies, Institute of Modelling Problems in Power Engineering, Ukrainian Academy of Sciences, Kiev, Ukraine, No.30, 2005, pp. 138–149.
  • [18] Bouguezel, S., Ahmad, M., and Swamy, M., New Parametric Discrete Fourier and Hartley Transforms, and Algorithms for Fast Computation, IEEE Transactions On Circuits And Systems, Vol. 58, No. 3, 2011, pp. 562–575.
  • [19] Puchala, D., Approximating the KLT by Maximizing the Sum of Fourth-Order Moments, IEEE Signal Processing Letters, Vol. 20, No. 3, 2013, pp.193–196.
  • [20] Stasiak, B. and Yatsymirskyy, M., Fast Orthogonal Neural Networks, In: Proc. of 8th International Conf. on Artificial Intelligence and Soft Computing (ICAISC’06), Lecture Notes in Computer Science 4029, 2006, pp. 142–149.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8915bb8b-bf6d-48c8-87b8-21084d75339f
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.