Shameless plug ahead.
A 1D $M$-band dual-tree wavelet toolbox can be found in 1D Wavelet decompositions : Matlab toolbox for 1D dual-tree M-band wavelet decomposition, and we just shared the 2D version embedded in a code for multivariate Gaussian noise image filtering in $M$-band 2D dual-tree (Hilbert) wavelet multicomponent image denoising. It is not as clean as I wished, I hope I can work on a specific version soon.
Actually, although the primal wavelet fiters are FIR, these wavelets are implemented in the Fourier domain, as for splines. We found that pure time- or space- FIR approximations for their implemetation included directional issues. We choosed $M$-band wavelets because they allow orthogonality, symmmetry and finite support. We suspect that the finer frequency decomposition is beneficial to the Hilbert transform, that works better on band-limited signals.
Another discussion can be found in What are the most popular wavelet or tight frame regularizers for image reconstruction problems? and in How to implement a $j$-level $M$-band wavelet transform of an image?
For the sake of diversity, you can find other ($2$-band) codes here.