A DCT-based multiscale framework for 2D greyscale image fusion using morphological differential features

Image fusion refers to the process of synergistic combination of useful sensory information from multiple images to synthesize a composite image with greater information content and increased practical value. It aims to maximize pertinent information specific to a sensor while minimizing uncertainty and redundancy in the fused output. In this paper, the authors have proposed a simple yet cohesive framework for 2D greyscale image fusion using morphological differential features. The features are extracted using morphological open-close filters applied at multiple scales using an isotropic structuring element which brings out categorical bright and dark features from the source images. At each scale, the bright (and dark) differential features are mutually compared using higher-valued AC coefficients obtained in the DCT domain within a block. The scale-specific fused features are recursively added to form an image containing high-frequency information from all conceivable scales. The fused image is achieved by superimposing the cumulative feature image onto a suitable base image. The base image is obtained by using a morphological weighted version of pseudomedian filter over the source images using the largest homothetic of the structuring element. The superiority of the framework is empirically verified in different domains of fusion, i.e. multi-focus, multi-sensor, multi-exposure, and multi-spectral image fusion. The proposed approach has surpassed the state-of-the-art unified fusion algorithms in terms of qualitative and quantitative evaluation with a perfect resource-time trade-off. Furthermore, the proposed method has been extended to greyscale-colour and colour-colour image pairs qualifying it for anatomical-functional image fusion.