Abstract
This paper presents an overview of the suitability of the polynomial spline wavelet transform to qualify as an elegant tool for multiresolution signal decomposition. The paper also reviews the effectiveness of the polynomial spline function that has maintained its leadership among its clan having wide applications in different areas ranging from the field of function approximation to computer vision. Construction of different scaling functions and their properties are shown. Polynomial spline wavelets and their characteristics are also shown. We have introduced the special cases of generalised spline wavelets. The distinctive features of these various representations are discussed with respect to the multiresolution signal decomposition. The main thrust is to emphasize a signal and image processing point of view which should make these techniques more attractive for mathematicians and signal processors.
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