How to limit the range of the super-resolved image

[antonelse]

Hi, just a curiosity.

What are the main parameters that influence the final output range if I do not consider the final scaling and rescaling thus the plain x here, without x = (x - self.mean) * self.img_range and x = x / self.img_range + self.mean.

Because I'm trying to use it with an image bounded between 0 and 1, but it gives output values from -30 to +200, and I don't know why. I'm doing this because I want to apply this algorithm to non-real images, like temperature or gas emissions maps.

If i force the net to output values bounded between 0 and 1, i.e. x[x<0]=0 and x[x>1]=1 I obtain images filled with ones, thus the training stops after some epochs since the loss is nearly constant and does not improve.

Thanks a lot!