Estimation of perceived image blur using edge features

Sharpness (or its complement, perceived blur or unsharpness) is an important attribute of image quality, and the spread of the physical blurring kernel is the predominant parameter determining that attribute. In this article we present an algorithm to estimate an objective measure for sharpness, called the blur index. The algorithm first estimates the physical parameter of blur spread from the blurred image and subsequently uses that estimate to compute the blur index. A global estimate of blur spread for the entire image is obtained by the weighted averaging of the local estimates of blur spread at prominent edge locations in the image. These local estimates at edges are obtained by nonlinearly combining local derivatives. The edge prominence is based on the edge height and the edge-contour length. The blur index is computed from the estimated blur spread by taking the sensitivity of the visual system to changes in the blur spread into account. The results of a psychophysical experiment in which subjects judged the unsharpness of natural images are also reported. By correlating the estimates of the blur index, as obtained from the algorithm, with the results obtained in the psychophysical experiment, we show that the blur index correlates well with the perceived unsharpness, and hence can be considered a psychometric measure of sharpness