A Variational Wavelet-Based Computational Model for the Enhancement of Contrast Perception in Color Images

One of the most delicate transformations in color image processing is contrast enhancement due to the fact that artifacts and unnatural colors can appear after the process. Here we propose a variational framework in which contrast enhancement is obtained through the minimization of a suitable energy functional of wavelet coefficients. We will show that this new approach has advantages with respect to the usual spatial techniques sustained by the fact that the wavelet representation is intrinsically local, multiscale and sparse. The computational complexity of the model is ${\cal O}(N)$ , N being the number of input pixels, and the algorithmic implementation is fast thanks to the fact that the functional minimum can be reached through few iterations of Newton-Raphson's method.