Performance measures for image fusion based on wavelet transform and curvelet transform

Curvelet transform is a recently-developed multi-scale transforms, which is more suitable for objects with curves. Applications of the curvelet transform have increased rapidly in the field of image fusion. Image fusion means the combining of two images into a single image that has the maximum information content without producing details that are non-existent in the given images. In the present work an algorithm for image fusion based on the curvelet transform was implemented, analyzed, and compared with a wavelet-based fusion algorithm. Two famous applications of image fusion are introduced; fusion of multi-focus images and fusion of multi-exposure images. Fusion results were evaluated and compared according to three measures of performance; the entropy (H), the mutual information (MI) and the amount of edge information (Q^AB/F). The three quantitative performance measures have shown that the curvelet based image fusion algorithm provides a slightly better fused image than the wavelet algorithm. In addition, the fused image has a better eye perception than the input ones.