RoES: Rotational Equivariant Selective-frequency Fusion for Multimodal Images

• URL: http://arxiv.org/abs/2609.12497v1
• Date: Fri, 11 Sep 2026 06:59:02 GMT
• Title: RoES: Rotational Equivariant Selective-frequency Fusion for Multimodal Images
• Abstract summary: Infrared-visible image fusion facilitates robust multimodal perception by integrating complementary textural nuances from visible sensors with thermal signatures from infrared systems.<n>We propose RoES, a Rotational Equivariant Selective-frequency fusion network.<n>We show that RoES consistently achieves state-of-the-art performance in both fusion quality and downstream object detection.
• Score: 12.816585131696549
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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