A Systematic Literature Review on Multimodal Image Fusion Models with Challenges and Future Research Trends

Imaging technology has undergone extensive development since 1985, which has practical implications concerning civilians and the military. Recently, image fusion is an emerging tool in image processing that is adept at handling diverse image types. Those image types include remote sensing images and medical images for upgrading the information through the fusion of visible and infrared light based on the analysis of the materials used. Presently, image fusion has been mainly performed in the medical industry. With the constraints of diagnosing a disease via single-modality images, image fusion could be able to meet up the prerequisites. Hence, it is further suggested to develop a fusion model using different modalities of images. The major intention of the fusion approach is to achieve higher contrast, enhancing the quality of images and apparent knowledge. The validation of fused images is done by three factors that are: (i) fused images should sustain significant information from the source images, (ii) artifacts must not be present in the fused images and (iii) the flaws of noise and misregistration must be evaded. Multimodal image fusion is one of the developing domains through the implementation of robust algorithms and standard transformation techniques. Thus, this work aims to analyze the different contributions of various multimodal image fusion models using intelligent methods. It will provide an extensive literature survey on image fusion techniques and comparison of those methods with the existing ones. It will offer various state-of-the-arts of image fusion methods with their diverse levels as well as their pros and cons. This review will give an introduction to the current fusion methods, modes of multimodal fusion, the datasets used and performance metrics; and finally, it also discusses the challenges of multimodal image fusion methods and the future research trends.