FusionRegister：每一对红外与可见光图像融合都值得配准
FusionRegister: Every Infrared and Visible Image Fusion Deserves Registration
提出一种由视觉先验引导的跨模态配准方法，可直接作用于融合结果，避免预配准步骤，为红外与可见光图像融合提供高效鲁棒的配准方案。
原文依据 a general cross-modality registration method guided by visual priors is proposed for infrared and visible image fusion task
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论跨不同视觉模态的空间配准是真实世界感知中多模态图像融合的关键但艰巨的步骤。尽管已有若干方法试图解决该问题，但现有的基于配准的融合方法通常需要大量的预配准操作，限制了其效率。为克服这些限制，本文提出一种由视觉先验引导的通用跨模态配准方法，用于红外与可见光图像融合任务，称为FusionRegister。首先，FusionRegister通过学习跨模态误配准表示而非强制对齐所有差异来实现鲁棒性，确保在挑战性输入条件下也能稳定输出。此外，FusionRegister通过直接作用于融合结果展现出强通用性，其中误配准被显式表示并有效处理，从而能够无缝集成到多种融合方法中，同时保持其内在特性。另外，通过将骨干融合方法作为自然的视觉先验提供者，引导配准过程仅关注不匹配区域，从而避免冗余操作，进一步提升了效率。在三个数据集上的大量实验表明，FusionRegister不仅继承了最优方法的融合质量，还提供了更优的细节对齐和鲁棒性，使其非常适合红外与可见光图像融合方法。代码将在 https://github.com/bociic/FusionRegister 发布。
英文原文摘要 1584 字符
Spatial registration across different visual modalities is a critical but formidable step in multi-modality image fusion for real-world perception. Although several methods are proposed to address this issue, the existing registration-based fusion methods typically require extensive pre-registration operations, limiting their efficiency. To overcome these limitations, a general cross-modality registration method guided by visual priors is proposed for infrared and visible image fusion task, termed FusionRegister. Firstly, FusionRegister achieves robustness by learning cross-modality misregistration representations rather than forcing alignment of all differences, ensuring stable outputs even under challenging input conditions. Moreover, FusionRegister demonstrates strong generality by operating directly on fused results, where misregistration is explicitly represented and effectively handled, enabling seamless integration with diverse fusion methods while preserving their intrinsic properties. In addition, its efficiency is further enhanced by serving the backbone fusion method as a natural visual prior provider, which guides the registration process to focus only on mismatch regions, thereby avoiding redundant operations. Extensive experiments on three datasets demonstrate that FusionRegister not only inherits the fusion quality of state-of-the-art methods, but also delivers superior detail alignment and robustness, making it highly suitable for infrared and visible image fusion method. The code will be available at https://github.com/bociic/FusionRegister.