RoES: Rotational Equivariant Selective-frequency Fusion for Multimodal Images

2026-09-11

AI Summary

为解决红外-可见光图像融合中频率特征处理不灵活的问题，提出RoES网络，通过动态分离高低频成分并采用旋转等变和双分支融合方法提高融合质量。

Abstract

Infrared-visible image fusion facilitates robust multimodal perception by integrating complementary textural nuances from visible sensors with thermal signatures from infrared systems. Due to the task's inherently ill-posed nature, existing methods heavily rely on structural priors but typically enforce rotation equivariance uniformly across all features. Such a holistic approach overlooks a critical distinction where low-frequency shared structures strictly adhere to equivariant constraints while high-frequency modality-specific details require greater flexibility to preserve unique information. To bridge this gap, we propose RoES, a Rotational Equivariant Selective-frequency fusion network. Instead of employing static decomposition, we introduce a trainable rotation-enhanced updater/predictor module to dynamically decouple low- and high-frequency components. The resulting representations are then processed through a dual-branch fusion module tailored for spectral consistency. Specifically, a rotation-equivariant Mamba is employed to capture long-range structural dependencies in the low-frequency domain, while a polar spectral attention-based Dual-Fourier block refines high-frequency details under explicit low-frequency guidance. Extensive experiments demonstrate that RoES consistently achieves state-of-the-art performance in both fusion quality and downstream object detection, establishing a robust solution for multimodal fusion by reconciling frequency-selective features with equivariant constraints. The source code is available at https://github.com/BryceLosky/RoES-Fusion.

Problem

• rotational equivariance
• multimodal image fusion
• frequency-selective
• infrared-visible

Innovation

• Rotational Equivariant
• Selective-frequency Fusion
• Dynamic Decoupling
• Dual-branch Fusion Module
• Polar Spectral Attention