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    $\begingroup$ Rescaling the way you do seems wrong, since a noise outlier at one image point completely changes the rescaled image globally. But the trouble starts before that: If $[0,1]$ is the natural interval for data, what sense does it make to model noise using naive addition? If an image point has value $1$, and there is $0.3$ noise, what does the calculation $1+0.3 = 1.3$ mean? It may be a wrong model for various reasons. An amateurish fix would be to model noise using something like addnoise[x_,noise_] := 1/Pi*ArcTan[Tan[Pi/2*(2*x-1)]+noise]+1/2, which maps $[0,1]$ to $[0,1]$. $\endgroup$ Commented Nov 8, 2022 at 15:39
  • $\begingroup$ A function you might find useful in the future is Rescale. eg foo = Rescale[nrctraindatat[[1]], MinMax@Flatten@nrctraindatat]; then foo === scaled (*True*) $\endgroup$ Commented Nov 8, 2022 at 16:04
  • $\begingroup$ related: mathematica.stackexchange.com/q/30091/60568 mathematica.stackexchange.com/q/159735/60568 $\endgroup$ Commented Nov 8, 2022 at 16:50
  • $\begingroup$ Possibly a dumb question, but would it make sense to use CosineDistance for this? $\endgroup$ Commented Dec 8, 2022 at 17:00
  • $\begingroup$ Also why aren't you just using Mathematica's built-in ImageDistance ? You also have RandomImage and ImageAdd so you don't need to use ImageData $\endgroup$ Commented Dec 3, 2023 at 22:25