The criterion of an evaluation method for fused images is critical to the performance of different image fusion algorithms. We present novel metrics for evaluation of fused images, based on the similarity of corresponding regions in images. The new metrics are computed on a region-by-region basis that is more suitable for evaluation, because human eyes are more sensitive to regions. The region information is represented by a feature matrix of the region, which consists of multifeature vectors including spatial information, texture, and gray value, which can adequately reflect the regional content. These make evaluation methods from the pixel level to the feature level. Research indicates that the proposed metrics are more consistent with the nature of human perception, as it considers the local image variations and the saliency of region. Experimental results show the effectiveness of the proposed metrics for the evaluation of fused images.