Wavelets: Time-Frequency Methods and Phase Space

By Jean Michel Combes, A. Grossmann, P. Tchamitchian, A. Pierce. 1992

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Citations in the corpus

Multi-Scale Context Aggregation by Dilated Convolutions (2015, methodology): “Most notably, the algorithme à trous for wavelet decomposition uses dilated filters (Holschneider et al., 1987; Shensa, 1992).”
Multiscale shape and detail enhancement from multi-light image collections (2007, methodology): “© 2007 ACM 0730-0301/2007/03-ART51 $5.00 DOI 10.1145/1239451.1239502 http://doi.acm.org/10.1145/1239451.1239502 Keywords: image enhancement, NPR, relighting, shape depiction, bilateral filter, multiscale image processing”
The redundant discrete wavelet transform and additive noise (2005, background): “Furthermore, the total noise distortion due to all subbands is found to be an equality rather than a bounding relationship involving frame bounds, which are shown, in fact, to widen as the number of decomposition scales increases.”
Deep Learning for Audio Signal Processing (2019, methodology): “convolution (also called atrous, or convolution with holes) [25], [27]–[29] can be used, which applies the convolutional filter over an area larger than its filter length by inserting zeros between filter coefficients.”
Reply to ``Comment on `Performance of different synchronization measures in real data: A case study on electroencephalographic signals' '' (2001, methodology): “as been recently introduced by Lachaux et al.[15, 34]. It is very similar to γ H, with the only diﬀerence that the phases are calculated by convolving each signal with a complex wavelet function Ψ(t) [33] Ψ(t) = (eiω0t −e−ω 2 0σ 2/2)·e−t2/2σ2 , (20) where w 0 is the center frequency of the wavelet a”
Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods (2005, background): “Conclusions are drawn in Section V. 0196-2892/$20.00 © 2005 IEEE”