A Unified Approach to Image Fusion: Comprehensive Review for Infrared and Visible Image Fusion Methods

The purpose of integrating the visible and infrared images is to generate a new image with a higher perception level for the human visual system. The fusion techniques play a vital role in surveillance, remote sensing, medical imaging, and military use since visible images provide rich texture and color information, whereas infrared images capture thermal data. However, modern fusion methods occasionally fail to achieve the perfect symmetry between noise removal and detail preservation, leading to loss of critical features. The latest infrared and visible image fusion methods are comprehensively discussed in this research and categorized into various categories, such as spatial domain, transform domain, hybrid approaches, and novel deep learning-based methods. Additionally, the impact of objective evaluation measures on various applications and how they are used to analyze fusion performance is discussed. Experimental research demonstrates that deep learning-based fusion techniques have greater flexibility and robustness, while traditional techniques dominate in maintaining the structure. This paper emphasizes the need for adaptive and application-oriented fusion approaches and discusses existing issues and presents future directions toward the improvement of fusion performance.