A single-captured image of a real-world scene is frequently insufficient to reveal all details. To address this problem

images of the same scene captured by multiple sensors or by the same sensor but with different properties are typically combined into a single image by using image fusion techniques. A novel technique based on fast discrete curvelet transform (FDCT) for improving image fusion quality is presented in this study. Source images are initially decomposed via FDCT. A new fusion rule is subsequently proposed to fuse low-frequency and high-frequency coefficients; this rule is unlike those in previous image fusion methods. Low-frequency coefficients are fused by local energy

whereas high-frequency coefficients are fused by sum-modified-Laplacian. The most important feature information can be selected as the fused coefficients by applying the fusion rule. Finally

inverse FDCT is applied to reconstruct the resultant image using the fused coefficients. Several images

including multimodal medical

infrared-visible

and multifocus images

are used in the experiments. Experimental results demonstrate that the proposed technique is better than traditional methods

such as pixel averaging

wavelet transform

and other state-of-the-art methods

including FDCT and the method presented based on bilateral gradient

in terms of both subjective and objective evaluations. The proposed fusion algorithm can obtain the most important feature information and exhibits superior performance to other methods in terms of multimodal and multifocus image fusion.