A comprehensive survey on image fusion: Which approach fits which need

Image fusion is a fundamental task in computer vision that involves combining information from multiple images to produce a more informative and consistent representation. Once the relevant features are identified, they are fused to achieve specific application goals. The field of image fusion encompasses several categories, including multi-focus, multi-exposure, multi-modal, and multi-view fusion. Most state-of-the-art solutions focus on optimizing methods to address a specific fusion category (e.g., multi-view, multi-modal, multi-exposure, or multi-focus). However, some use cases require universal methods capable of handling all these challenges. While recent advancements, particularly in deep learning, have achieved remarkable results within individual categories, the growing need for general-purpose solutions across diverse fusion tasks calls for a broader perspective. This survey provides a comprehensive and unified review of image fusion techniques, systematically covering all four major categories. Special attention is given to deep learning-based methods, which have become dominant in recent years across various fusion types. A key contribution of this work is the integration of multi-view image fusion, often overlooked in prior surveys, with other fusion approaches. We introduce a novel taxonomy that distinguishes between mono-category methods, which target a single fusion domain, and multi-category methods, which are capable of addressing multiple fusion types. Particular emphasis is placed on generalist multi-category approaches, which handle cross-domain scenarios and represent a promising line of research. Additionally, this survey provides practical guidance through summaries of available datasets, evaluation metrics, and representative methods. By offering this structured overview and highlighting unexplored directions, the survey serves as both a foundational reference and a roadmap for future research on unified and adaptive deep learning-based image fusion techniques. • Classification of fusion methods into mono- and multi-category approaches. • Identification of multi-category fusion methods with unified architectures. • Evaluation of fusion performance using benchmark datasets and objective metrics. • Discussion of challenges in generalization, interpretability, and multi-view fusion.