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This package is a Julia extension package to Wavelets.jl (WaveletsExt is short for Wavelets Extension). It contains additional functionalities that complement Wavelets.jl, namely
- Multi-dimensional wavelet transforms
- Redundant wavelet transforms
- Best basis algorithms
- Denoising methods
- Wavelet transform based feature extraction techniques
This package is written and maintained by Zeng Fung Liew and Shozen Dan under the supervision of Professor Naoki Saito at the University of California, Davis.
The package is part of the official Julia Registry. It can be install via the Julia REPL.
(@1.7) pkg> add WaveletsExt
or
julia> using Pkg; Pkg.add("WaveletsExt")
Load the WaveletsExt module along with Wavelets.jl.
using Wavelets, WaveletsExt
[1] Coifman, R.R., Wickerhauser, M.V. (1992). Entropy-based algorithms for best basis
selection. DOI: 10.1109/18.119732
[2] Saito, N. (1998). The least statistically-dependent basis and its applications. DOI:
10.1109/ACSSC.1998.750958
[3] Beylkin, G., Saito, N. (1992). Wavelets, their autocorrelation functions, and
multiresolution representations of signals. DOI:
10.1117/12.131585
[4] Nason, G.P., Silverman, B.W. (1995) The Stationary Wavelet Transform and some
Statistical Applications. DOI:
10.1007/978-1-4612-2544-7_17
[5] Donoho, D.L., Johnstone, I.M. (1995). Adapting to Unknown Smoothness via Wavelet
Shrinkage. DOI:
10.1080/01621459.1995.10476626
[6] Saito, N., Coifman, R.R. (1994). Local Discriminant Basis. DOI:
10.1117/12.188763
[7] Saito, N., Coifman, R.R. (1995). Local discriminant basis and their applications. DOI:
10.1007/BF01250288
[8] Saito, N., Marchand, B. (2012). Earth Mover's Distance-Based Local Discriminant Basis.
DOI: 10.1007/978-1-4614-4145-8_12
[9] Cohen, I., Raz, S., Malah, D. (1997). Orthonormal shift-invariant wavelet packet
decomposition and representation. DOI:
10.1016/S0165-1684(97)00007-8
[10] Irion, J., Saito, N. (2017). Efficient Approximation and Denoising of Graph Signals
Using the Multiscale Basis Dictionaries. DOI: 10.1109/TSIPN.2016.2632039
- nD wavelet transforms for redundant and non-redundant versions