Tutorial on 2nd generation wavelets (with lifting)? For some denoising and deconvolution experiments, I'd like to apply a 2nd generation wavelet transform (using lifting steps) to images. 

I know that there are several implementations available, but most of them use matlab, while I want to work in C++ with OpenCV.
Since there is no built-in wavelet transform implementation in OpenCV 2.x, I plan to implement it myself (plus, it will make a good exercise for me).
After some research, I've been able to find the original articles about the 2nd generation transform, but I'm still a bit confused about the exact way the algorithm works.

Taking for main reference the paper [1] by Sweldens: The Lifting scheme: a construction of second generation wavelets, I'm still confused by the definition of the index sets $\mathcal{K}(j)$ : what is their size ? how are they built ? ...

Hence my question : does anyone know about some resources about the 2nd generation wavelet transform (papers, tutorials, slides...) that are either in a tutorial-like form, or that provide a more algorithmic view (rather than a mathematical one), which would help me designing my own implementation ?

Thank you in advance.

References

My main reference is:

[1] Sweldens, W. (1998). The lifting scheme: A construction of second generation wavelets. SIAM Journal on Mathematical Analysis, 29(2), 511.

And I am also learning from:

[2] Daubechies, I., &amp; Sweldens, W. (1998). Factoring wavelet transforms into lifting steps. Journal of Fourier analysis and applications, 4(3), 247–269.

[3] Kovacevic, J., &amp; Sweldens, W. (2000). Wavelet families of increasing order in arbitrary dimensions. Image Processing, 9(3), 480–496. doi:10.1109/83.826784
I've bought finally a copy of [Ripples in Mathematics
The Discrete Wavelet Transform][1], and I'm very pleased by this book. The authors explain the DWT with alternating point of views (lifting schemes, filter banks approach, multi-resolution analysis), where each of these viewpoints has its own advantages. Furthermore, the book is implementation oriented, with chapters about boundaries handling and matlab/C implementations.

I'm still looking on a proper way to handle odd-sized signals, but Ripples gave me a good start.

[1]: http://www.control.auc.dk/~alc/ripples.html "Ripples in Mathematics The Discrete Wavelet Transform", by Arne Jensen and Anders la Cour-Harbo