Development of nth DimensionalBi-Orthogonal Wavelets Transform and Its Applications

Abstract

This paper describes that key idea to introduce wavelets transform, one-.two-and. three-dimensional wavelets transforms are introduced by means of Daubechies 2nd order base function. Finally, nth order bi-orthogonal wavelets transform algorism is derived in terms of tensor notation. As a result, it is clarified the mathematical as well as physical meaning of the discrete wavelets transform. Thus, this paper suggests that various signal and image processing problems can be carried out by the discrete wavelets transform.

Modal-Wavelet Transform as a Smart Visualization Tool

“…Data handling technologies based on the digital computers are of main importance to realize more efficient networking and computing. Discrete wavelet transform (DWT) may be promised to become a deterministic methodology handling the digital signals and images, e.g., compressing data quantity, extracting their characteristics, etc (e.g., Matsuyama, 1999). Moreover, their applications to electromagnetic field calculation, solving forward and inverse problems, have been investigated and spurred to faster calculation algorithm (Beylkin et al, 1991, Doi et al, 1996.…”