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Image normalization ensures optimal comparisons across data acquisition methods and texture instances. The normalization of pixel values (intensity) is recommended for imaging modalities that do not correspond to absolute physical quantities.

As a data scientist or software engineer, you might be working with image datasets that need to be normalized before they can be used for machine learning tasks. Normalizing the data ensures that the model receives consistent input, making it easier to train and improve its accuracy.

Normalization is used when the data doesn't have Gaussian distribution whereas Standardization is used on data having Gaussian distribution. Normalization scales in a range of [0,1] or [-1,1]. Standardization is not bounded by range. Normalization is highly affected by outliers.

The normalization of an image consists in dividing each of its pixel values by the maximum value that a pixel can take (255 for an 8-bit image, 4095 for a 12-bit image, 65 535 for a 16-bit image).

If you see a bell-curve in your data then standardization is more preferable. For this, you will have to plot your data. If your dataset has extremely high or low values (outliers) then standardization is more preferred because usually, normalization will compress these values into a small range.

The normalization of an image consists in dividing each of its pixel values by the maximum value that a pixel can take (255 for an 8-bit image, 4095 for a 12-bit image, 65 535 for a 16-bit image).

Standardized images are resulted by subtracting the mean pixel values from their individual pixel values and then dividing them by the standard deviation of the values of pixels.

Data normalization is typically done by subtracting the mean (the average of all pixel values) from each pixel, and then dividing the result by the standard deviation of all the pixel values.