A family of polynomial spline wavelet transforms
Signal Processing
Digital image processing
Shape Analysis and Classification: Theory and Practice
Shape Analysis and Classification: Theory and Practice
Filter design for CWT computation using the Shensa algorithm
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
Rapid computation of the continuous wavelet transform by obliqueprojections
IEEE Transactions on Signal Processing
Fast CWT computation at integer scales by the generalized MRAstructure
IEEE Transactions on Signal Processing
Least-squares image resizing using finite differences
IEEE Transactions on Image Processing
The convolution theorem for the continuous wavelet tranform
Signal Processing
Need for speed: fast Stockwell transform (FST) with O(N) complexity
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Spatially variant convolution with scaled B-splines
IEEE Transactions on Image Processing
Fast space-variant elliptical filtering using box splines
IEEE Transactions on Image Processing
Observing damaged beams through their time-frequency extended signatures
Signal Processing
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The continuous wavelet transform (CWT) is a common signal-processing tool for the analysis of nonstationary signals. We propose here a new B-spline-based method that allows the CWT computation at any scale. A nice property of the algorithm is that the computational cost is independent of the scale value. Its complexity is of the same order as that of the fastest published methods, without being restricted to dyadic or integer scales. The method reduces to the filtering of an auxiliary (pre-integrated) signal with an expanded mask that acts as a kind of modified a trous' filter. The algorithm is well-suited for a parallel implementation.