I recently was asked for suggestions for how to create slow interpolations between noisy matrixes in Jitter. The problem when interpolating between noisy matrixes is that the result will tend to be grayed out, with less contrasts and extreme pixel values than the two matrixes that we start out with.
In this patch I’m applying some statistics to compensate for the lack of contrast in the interpolated matrixes. All processing is done on matrixes of @type float32. First I find the mean value of the matrix, and change it to become 0.0. Next I calculate the standard deviation. This is used to normalize the the matrix to have a standard deviation of 0.28871. From some empiric measurements of a large noisy matrix, that seemed to be a common value for the standard deviation. Finally I up the mean value of the matrix to 0.5 again.
The patch can be downloaded here.