Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption

TL;DR

This survey comprehensively reviews deep learning-based infrared-visible image fusion methods, analyzing their technical approaches, performance, and future challenges to guide ongoing research in this rapidly evolving field.

Contribution

It provides a multi-dimensional framework and detailed analysis of recent IVIF methods, addressing data compatibility, task adaptation, and performance evaluation.

Findings

Deep learning approaches have advanced IVIF performance.

Data compatibility and task adaptability are key challenges.

Performance varies across registration, fusion, and high-level tasks.

Abstract

Infrared-visible image fusion (IVIF) is a critical task in computer vision, aimed at integrating the unique features of both infrared and visible spectra into a unified representation. Since 2018, the field has entered the deep learning era, with an increasing variety of approaches introducing a range of networks and loss functions to enhance visual performance. However, challenges such as data compatibility, perception accuracy, and efficiency remain. Unfortunately, there is a lack of recent comprehensive surveys that address this rapidly expanding domain. This paper fills that gap by providing a thorough survey covering a broad range of topics. We introduce a multi-dimensional framework to elucidate common learning-based IVIF methods, from visual enhancement strategies to data compatibility and task adaptability. We also present a detailed analysis of these approaches, accompanied by a lookup table clarifying their core ideas. Furthermore, we summarize performance comparisons, both quantitatively and qualitatively, focusing on registration, fusion, and subsequent high-level tasks. Beyond technical analysis, we discuss potential future directions and open issues in this area. For further details, visit our GitHub repository: https://github.com/RollingPlain/IVIF_ZOO.