  We make use of recent results from random matrix theory to identify a derived
threshold, for isolating noise from image features. The procedure assumes the
existence of a set of noisy images, where denoising can be carried out on
individual rows or columns independently. The fact that these are guaranteed to
be correlated makes the correlation matrix an ideal tool for isolating noise.
The random matrix result provides lowest and highest eigenvalues for the
Gaussian random noise for which case, the eigenvalue distribution function is
analytically known. This provides an ideal threshold for removing Gaussian
random noise and thereby separating the universal noisy features from the
non-universal components belonging to the specific image under consideration.
