Image fusion involves merging two or more images in such a way as to retain the most desirable characteristics of each.
There are various image fusion methods and they can be classified into three main categories: i) Spatial domain, ii)
Transform domain, and iii) Statistical domain. We focus on the transform domain in this paper as spatial domain
methods are primitive and statistical domain methods suffer from a significant increase of computational complexity. In
the field of image fusion, performance analysis is important since the evaluation result gives valuable information which
can be utilized in various applications, such as military, medical imaging, remote sensing, and so on. In this paper, we
analyze and compare the performance of fusion methods based on four different transforms: i) wavelet transform, ii)
curvelet transform, iii) contourlet transform and iv) nonsubsampled contourlet transform. Fusion framework and scheme
are explained in detail, and two different sets of images are used in our experiments. Furthermore, various performance
evaluation metrics are adopted to quantitatively analyze the fusion results. The comparison results show that the
nonsubsampled contourlet transform method performs better than the other three methods. During the experiments, we
also found out that the decomposition level of 3 showed the best fusion performance, and decomposition levels beyond
level-3 did not significantly affect the fusion results.