Maximum local energy: An effective approach for multisensor image fusion in beyond wavelet transform domain

The benefits of multisensor fusion have motivated research in this area in recent years. Redundant fusion methods are used to enhance fusion system capability and reliability. The benefits of beyond wavelets have also prompted scholars to conduct research in this field. In this paper, we propose the maximum local energy method to calculate the low-frequency coefficients of images and compare the results with those of different beyond wavelets. An image fusion step was performed as follows: first, we obtained the coefficients of two different types of images through beyond wavelet transform. Second, we selected the low-frequency coefficients by maximum local energy and obtaining the high-frequency coefficients using the sum modified Laplacian method. Finally, the fused image was obtained by performing an inverse beyond wavelet transform. In addition to human vision analysis, the images were also compared through quantitative analysis. Three types of images (multifocus, multimodal medical, and remote sensing images) were used in the experiments to compare the results among the beyond wavelets. The numerical experiments reveal that maximum local energy is a new strategy for attaining image fusion with satisfactory performance.