Image normalization

There are 3 ways to normalize regular data:
1. Get the maximum and minimum from the dataset, follow the equation to normlize each element (x - min)/(max - min).

2. Use a known maximum value to normalize data: x/Max;

3. Z normalization: estimate the mean and standard deviation, and normalize the population to a normal distribution with 0 mean and 1 standard deviation: (x - mean)/deviation.

But how to normalize images?

The usage is given as follows:

We have a classifier optimized on certain type of images, which means the histogram the classifier can handle is determined to that type of distribution. For images from other sources, we need to transform the original image to an image that fit the histogram of the trained source. Now, we get a desired image and an original version of thesame image, use histogram specification to generate a look up table to do the remapping.

A question ticked in: after transforming, the useful features whitened out because histogram specification tries to even the histogram, thus those features with with less population get compressed.

What shall I do? use linear transformation? Is it a good idea?