Undecimated Wavelet Transform (a trous algorithm) - how to determine 'anchor'/'center' of convolution filter i am currently implementing the 'Undecimated Wavelet Transform' with the 'a trous' algorithm. See e.g. http://www.znu.ac.ir/data/members/fazli_saeid/DIP/Paper/ISSUE2/04060954_2.pdf, section II-A.
As far as I understand, the implementation (in 1D) for the 'decomposition' and 'reconstruction' phase is very simple, it boils down to the convolution with a low-pass and high-pass filter (where for levels > 0, one has to add zeros between the coefficients of the filters).
E.g. for the decomposition filter of the 'Bior1.3' wavelet, i have the following filters for level 0, 1, 2 of size 6, 11 (6+5) and 16 (6 + 10):
level 0: [c1 c2 c3 c4 c5 c6]
level 1: [c1 0 c2 0 c3 0 c4 0 c5 0 c6]
level 2: [c1 0 0 c2 0 0 c3 0 0 c4 0 0 c5 0 0 c6]
I hope I inserted the zeros on the right places, if not please correct me.
The actual values of the coefficients c1, c2, c3, c4, c5, c6 can be found at http://wavelets.pybytes.com/wavelet/bior1.3/.
My questions is now, what is the 'anchor' of this filter kernels, how can the anchor be calculated in a general way (for decomposition/reconstruction filters of different wavelet classes) ?
I suppose its something like 'anchor_index = round_down(kernel_size / 2)', when anchor_index is 0-indexed, but i'm not sure.
Note that for implementing a convolution with a certain kernel, one has to know always the 'anchor' of the kernel  (the index of the coefficient in the filter which is multiplied with the 'current' array element). 
 A: See the 1D code (and 2D image code is available also) at 
http://www.sparsesignalrecipes.info
Chapter 3, in IDL star1d.pro, star2d.pro - using B3 spline à trous wavelet transform.
A: Decimation means throwing away samples. In the ordinary Discrete Wavelet Transform (DWT) the filters are designed such that Perfect Reconstruction (PR) is possible to achieve even if the result of the convolutions are down-sampled a factor of 2. Down-sampling a factor of two means throwing away every second sample. The procedure is then iterated on the low-pass part of the signal. 
If down-sampling is not done, then $[c_1,0,c_2,0,c_3,\cdots]$ will be the filter to apply on the next level. Since the ordinary is DWT dyadic, the next step will be $[c_1,0,0,0,c_2,0,0,0,c_3,\cdots]$, where every fourth position is a filter coefficient. Next level it will be every eight, and so on.
If you think about it a while, you will realize that the un-decimated transform is able to capture all shift-variances of the signal which the decimated transform is not able to capture.
