Title: Gradient-based Filter Design for the Dual-tree Wavelet Transform,
Abstract: The wavelet transform has seen success when incorporated into neural network
architectures, such as in wavelet scattering networks. More recently, it has
been shown that the dual-tree complex wavelet transform can provide better
representations than the standard transform. With this in mind, we extend our
previous method for learning filters for the 1D and 2D wavelet transforms into
the dual-tree domain. We show that with few modifications to our original
model, we can learn directional filters that leverage the properties of the
dual-tree wavelet transform.