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Design of orthonormal and overcomplete wavelet transforms based on rational sampling factors
Wavelet Applications in Industrial Processing V
Most wavelet transforms used in practice are based on integer sampling factors. Wavelet transforms based on rational sampling factors offer in principle the potential for time-scale signal representations having a finer frequency resolution. Previous work on rational wavelet transforms and filter banks includes filter design methods and frequency domain implementations. We present several specific examples of Daubechies-type filters for a discrete orthonormal rational wavelet transform (FIRdoi:10.1117/12.741073 fatcat:4ts6cn7arfbg5baxiubzxfft74