Image normalization refers to eliminating image variations (such as
noise, illumination, or occlusion) that are related to conditions of
image acquisition and are irrelevant to object 
identity. Image normalization can be used as a preprocessing stage to
assist computer 
or human object perception. In this paper, a class-based image
normalization method is 
proposed. Objects in this method are represented in the PCA basis, and
mutual information is used to identify irrelevant principal
components. These components are then discarded to obtain a normalized
image which is not affected by the specific conditions of image
acquisition. 
The method is demonstrated to produce visually pleasing
results and to improve significantly the accuracy of
known recognition algorithms. 

The use of mutual information is a significant advantage over the
standard method of discarding components according to the eigenvalues,
since eigenvalues  
correspond to variance and have no direct relation to the relevance of
components to representation.
An additional advantage of the
proposed algorithm is that many types of image
variations are handled in a unified framework.