Multimodal medical image fusion using modified weight maps and guided filter

In the recent years multimodal medical image fusion has evolved out as an active area of research, and it is finding a lot of applications in several imaging areas. The main aim is to collect the required relevant information from the source input images into a single output image. Among all the prevailing techniques, guided filter has emerged out as best edge preserving technique. In this paper, a framework using guided filter by modifying saliency and weight maps have been used. The simulated results shows the efficiency of the proposed technique for capturing the details of multimodal images by smoothening the noise and preserving the edges.