Structure similarity based objective metric for pixel-level image fusion

Pixel-level image fusion has been investigated in various applications and many algorithms have been proposed. However, few authors have addressed the problem of how to evaluate the performance of those algorithms. In this paper, we propose a structure similarity based objective metric for image fusion, which evaluates the similarity of the gradient fields between the source images and the fused result. Meanwhile, local information given by salience within localized windows are incorporated into the final metric. Extensive experiments demonstrate that the proposed metric can evaluate the image fusion performance effectively and is consistent with perceptually obtained results.