The paper describes a novel comprehensive approach to image-contrast enhancement in the spatial domain. Instead of defining another transformation function our strategy consists in adopting a general functional form, able to map different transformation functions, and in using a learning technique to select the parameter values that are optimal for the image being processed.First, local measures of spatial activity are assigned to each pixel of the image. Second, the local contrast value for each pixel is computed according to a function which is based on human visual response. Third, the parameters of a comprehensive contrast-enhancement function are selected by a genetic algorithm on the basis of the spatial activity of the image resulting from the transformation.The validity of the proposed technique is confirmed both perceptually, that is, higher fitness values correspond to the images that have been judged better by human observers, and by comparative evaluations of our algorithm with respect to classical methods.