<p>In image fusion, it is seen that most existing fusion-based algorithms work better for one application but are limited in terms of performance for other applications. So, to address this issue, a new image fusion algorithm based on the combined approach of Hilbert vibration decomposition (HVD) and non-subsampled contourlet transform (NSCT) is proposed for multiple applications. Initially, the acquired source images are treated by a simplified image enhancement technique to minimize the fusion losses in the output. By carefully investing the input image’s histogram, the image is divided into the background gray-level sub-image and non-background gray-level sub-image. These images are further subdivided based on the mean. Following this, a boundary value on the histogram of these divisions is assigned to avoid unwanted washed-out and patchiness effects. After this, the source images are decomposed into a set of instantaneous image amplitudes by HVD. Then, two different fusion rules are implemented to the set of instantaneous coefficients obtained by NSCT for each image amplitude. Inverse transform criteria combine these fused coefficients to generate the final integrated image. To validate the adopted standards, experiments are carried out for three critical applications: medical, multi-focus, and visible-infrared fusion. The results of publicly available datasets are compared with thirty-four state-of-the-art algorithms, and it is seen that by using the advantageous effects of both HVD and NSCT, the proposed approach demonstrates better/comparable outputs for multiple applications in terms of subjective and objective evaluation than previous fusion-based algorithms.</p>