Robust Multi-Modal Image Fusion under Noisy and Adverse Conditions – A Study

Robust multi-modal Image fusion under noisy and adverse conditions is crucial for enhancing the reliability of real-world systems in fields such as surveil-lance, autonomous vehicles, and medical diagnostics. As the scale and diversity of sensors increase, scalability becomes an issue necessitating efficient fusion algorithms capable of managing highdimensional data. Real-world environments are dynamic and require adaptive fusion methods for responding dynamically based on changing conditions. Handling sensor-specific noise and artifacts remains complex due to the distinct characteristics of each sensor type. This work highlights a study of major works reported in the literature with key challenges.