I currently have a large matrix M (~100x100x50 elements) containing both positive and negative values. At the moment, if I want to smooth this matrix, I use the smooth3 function to apply a gaussian kernel over the entire 3-D matrix.
What I want to achieve is a variable level of smoothing within this matrix - i.e.. different parts of the matrix M are smoothed to different levels of sigma depending of the value in a similar 3-D matrix, d (with values ranging from 0 to 1). Where d is 0, no smoothing occurs, where d is 1 a maximum level of smoothing occurs.
The fact that the matrix is 3-D is trivial. Smoothing in 3 dimensions is nice, but not essential, and my current code (performing various other manipulations) handles each of the 50 slices of M separately anyway. I am happy to replace smooth3 with a convolution of M with a gaussian function, and perform this convolution over each slice individually. What I can't figure out is how to vary the sigma level of this gaussian function (based on d) given its location in M and output the result accordingly.
An alternative approach may be to use matrix d as a mask for a very smooth version of matrix Ms and somehow manipulate M and Ms to give an equivalent result, however I'm not convinced that this will work as I can't think of a function to combine M and Md that won't give artefacts of each of M or Ms when 0 < d < 1...any thoughts?
[I'm using 2009b, and only have access to the Signal Processing toolbox.]
