Color image sharpening inspired by human vision models

Abstract

A method to sharpen digital color images that takes viewing conditions and human vision models into consideration is described. The method combines the Laplacian of Gaussian (LoG) operator with spatial filters that approximate the contrast sensitivity functions of human visual systems. The sharpening operation is introduced in the opponent color space, following the scheme proposed in the spatial extension of CIELAB (S-CIELAB). We deduce the modification of the original image necessary to obtain the spatially filtered image that approaches the perceived LoG-sharpened image for given viewing conditions. At short viewing distances, for which the spatial blurring is small, most fine edges and object contours are sharpened. At long distances, for which the spatial blurring is greater, only large figures are sharpened. Because of the smoothing Gaussian functions involved in the LoG operator, the proposed image sharpening does not tend to increase noise. When the sharpening operation is limited to the achromatic channel, the results are good. This is consistent with the high importance attached to the luminance channel in the spatial content of color images. Image sharpening based on only the Laplacian of the original is not sensitive to variations of viewing conditions, tends to increase noise, and suffers from its appearance deteriorating rather quickly with the depth of the sharpening operation.