Advances and challenges in infrared-visible image fusion: a comprehensive review of techniques and applications

Abstract Infrared–visible image fusion (IVIF) integrates complementary thermal and photometric cues for surveillance, remote sensing, and autonomous perception. Existing surveys, while comprehensive, provide limited guidance for design-to-deployment and seldom relate fusion quality to task outcomes or device constraints. This work provides a unified perspective that organizes IVIF methods along an interface-attention-alignment coordinate system covering classical spatial/transform pipelines and contemporary deep paradigms (generative, discriminative, multi-task, hybrid/Transformer, dynamic). Building on literature through 2025, we synthesize fidelity-robustness-efficiency trade-offs and introduce a comparison-to-deployment protocol that couples fusion metrics with task accuracy (AP/mIoU), latency, memory footprint, and condition-performance characterization (misregistration, noise, illumination/weather). We consolidate Transformer/hybrid coverage with practical recipes and focused guidance on temporal consistency, robustness auditing, and physics-grounded interpretability. Compared with previous reviews, our survey concurrently addresses four under-covered dimensions-video temporal consistency, robustness auditing, task-aware evaluation, and deployment reporting-and distills a practical checklist linking architectural choices to operating conditions and hardware budgets, enabling reproducible, task-relevant IVIF practice.