When the Unet is trained, the input is normalized and with the mean and std of this input, the target is also normalized. Why do you do this? I am making a neural network that circumvents the Inverse fast Fourier transform (IFFT), so that you can just use one big neural network on the masked images. However, I don’t know what to do with the normalization of the images, since it happens right after the IFFT. Maybe you can help me out?