A Survey of Multi-Modal Image Fusion Methodologies

Abstract

Digital image fusion has advanced significantly in governments and civil domains since its introduction in the late 1980s, certainly image fusion of infrared light, materials characterization, remote sensing data fusion, visions segmentation techniques, and brain tumor detection fusion. In medical diagnostics, imaging technology is critical. Because single medical pictures cannot match the demands of diagnostic techniques, which necessitate a huge quantity of data, image fusion study has become a hot subject. Single-mode integration and multi - modal fusion is the two types of medical image processing. Due to the limitations of single-modal fusion's data, many scientists are investigating multidimensional fusion. Brain tumor detection fusion represents the operations of integrating multiple images from imaging modality to formulate fused images with larger volume of data, allowing medical images to be more clinically useful. In this article, we focus on providing a survey of multi-modal image fusion approaches with central focus on novel developments in the domain based on the present fusion approaches, incorporating deep learning fusion approaches. Lastly, this concludes that contemporary multi-modal image fusion study findings are significantly fundamental, and the development trends is on the increase, however there are several hurdles in the study area.