This is in the demo (in PTAk):
where snr is defined as te range of te original image divided by the standard deviation of the Gaussian noise added.
in the demo is called SVDgen
smoofun <- rep(list(Susan1D),7)
d.svdo <- SVDgen(timage12,nomb=25,smoothing=TRUE,smoo =list(smoofun))
The smoother in each the x and y orientations is a combination of a kernel regression (i.e."physical" neighbours) and non-linear kernels moothing (i.e. value neighbours).