I have a dynamic range of 256x256 matrices. I want to have a CNN based binary classifier. The matrices are images with a very wide range of intensities (10 orders of magnitude).
I am afraid to use Mean Normalization or Min-Max Scaling, because it will introduce negative values into the feature space:
$x- \bar{x} \over \sigma$ or $\frac{x - \bar{x}}{\text{max}(x)-\text{min}(x)}$
Assume I am using z-score normalization. All the negative values will go to zero after the first activation layer. Isn't it true? So basically I am loosing all the information in the regions at which the signal is bellow the average.