A Comprehensive Survey on Image Fusion: Which Approach Fits Which Need
Résumé
Image fusion is a crucial domain within computer vision, focusing on integrating elements from multiple images to extract complementary information while eliminating redundancy. Once the relevant features are identified, they are combined 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 that can handle all these challenges. The purpose of this review is to provide an in-depth and detailed analysis of various image fusion categories to thoroughly understand these domains. Additionally, this survey aims to integrate multi-view image fusion methods into a comprehensive overview of image fusion, which is not commonly addressed in the existing literature. The goal is to highlight multi-category methods that can tackle image fusion problems involving images from different types of fusion categories. Finally, potential directions for advancing this category of methods will be proposed, alongside the various challenges that this field faces.This survey examines each image fusion category to gain a better understanding of the issues related to multi-category methods. It contributes to the field of image fusion and offers researchers valuable insights into developing more effective multi-category solutions.
| Origine | Publication financée par une institution |
|---|---|
| Licence |
![]()
Cite hal-04637094 Objet présenté à une conférence Bernardi Gwendal, David Strubel, Brisebarre Godefroy, Jean François Garin, Mohsen Ardabilian, et al.. Image Fusion Survey: A Novel Taxonomy Integrating Transformer and Recent Approaches. ICPR 2024 Workshop on Multi- and Cross-Modal Information for Enhanced Pattern Recognition (MCMI), 2024, Calcuta, India. ⟨hal-04637094⟩