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I'm wondering about data normalization in CNN, how can we do it for the input images?, what can it add to the model's performance? and what are the main pre-processing techniques before doing the convolutional layer?

Happy to get extra-reading, thanks.

UPDATE:

I'm looking for how to practically normalize the data inputs, I didn't get it! . Additional reading (explanation, papers) will be so helpful.

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    $\begingroup$ Also stats.stackexchange.com/questions/336458/… and stats.stackexchange.com/questions/185853/… $\endgroup$
    – Sycorax ♦
    Commented Apr 26, 2019 at 20:16
  • $\begingroup$ I have updated the question, I didn't understand the normalization procedure, thanks for help $\endgroup$
    – Khalil Meg
    Commented Apr 27, 2019 at 21:17
  • $\begingroup$ The three questions that I've linked to appear to address this question. Can you elaborate what you don't understand? "I don't get it!" is not an answerable question. $\endgroup$
    – Sycorax ♦
    Commented Apr 27, 2019 at 23:31

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Normalizing the data prior to CNN will create more spherical error surfaces which will yield faster convergence of the gradient-based optimization procedure. If this step is skipped, some axes (determined by the eigenvectors of the loss function) will have steeper/flatter structures and consequently, the travel of our optimizer on the surface will get troubled.

You can standardize the images. However, generally, we map the images into 0-1 interval (by dividing to 255 for gray images) so that the values are bounded.

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