Learning human preferences to sharpen images

We propose an image sharpening method that automatically optimizes the perceived sharpness of an image. Image sharpness is defined in terms of the one-dimensional contrast across region boundaries. Regions are automatically extracted for all natural scales present that are themselves identified automatically. Human judgments are collected and used to learn a function that determines the best sharpening parameter values at an image location as a function of certain local image properties. We use the Gaussian mixture model (GMM) to estimate the joint probability density of the preferred sharpening parameters and local image properties. The latter are then adaptively estimated by parametric regression from GMM. Experimental results demonstrate the adaptive nature and superior performance of our approach over the traditional Unsharp Masking method.