This is in the demo (in PTAk): >demo.SVDgen(snr=3,openX11s=T) 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). |
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