Perceptual Contrast-Based Image Fusion: A Variational Approach

Abstract

Local contrast, or variation, plays an important role in image fusion which is mainly to preserve the important information from the source images to the fusion result. Weber's law tells us that the same variation under different backgrounds will cause different perceptual feelings, thus an ideal image processor has to take into account the effects of vision psychology and psychophysics. This paper considers the property of human visual system (HVS) and transfers the quantitative perceptual variations from each source image to the result. Using just-noticeable-difference (JND) as measurement, the multiband image's perceptual contrast is obtained as a target. We construct a functional extremum problem to find a single band image, or fusion result, which has the closest perceptual contrast to the target one. Via the variational approach, the Euler-Lagrange equation is derived, and a gradient descent iteration is employed. Experimental results show that this method is perceptually good.