Coupled Degradation Modeling and Fusion: A VLM-Guided Degradation-Coupled Network for Degradation-Aware Infrared and Visible Image Fusion

• URL: http://arxiv.org/abs/2510.11456v1
• Date: Mon, 13 Oct 2025 14:26:33 GMT
• Title: Coupled Degradation Modeling and Fusion: A VLM-Guided Degradation-Coupled Network for Degradation-Aware Infrared and Visible Image Fusion
• Authors: Tianpei Zhang, Jufeng Zhao, Yiming Zhu, Guangmang Cui,
• Abstract summary: We propose a novel VLM-Guided Degradation-Coupled Fusion network (VGDCFusion)<n>Our VGDCFusion significantly outperforms existing state-of-the-art fusion approaches under various degraded image scenarios.
• Score: 9.915632806109555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing Infrared and Visible Image Fusion (IVIF) methods typically assume high-quality inputs. However, when handing degraded images, these methods heavily rely on manually switching between different pre-processing techniques. This decoupling of degradation handling and image fusion leads to significant performance degradation. In this paper, we propose a novel VLM-Guided Degradation-Coupled Fusion network (VGDCFusion), which tightly couples degradation modeling with the fusion process and leverages vision-language models (VLMs) for degradation-aware perception and guided suppression. Specifically, the proposed Specific-Prompt Degradation-Coupled Extractor (SPDCE) enables modality-specific degradation awareness and establishes a joint modeling of degradation suppression and intra-modal feature extraction. In parallel, the Joint-Prompt Degradation-Coupled Fusion (JPDCF) facilitates cross-modal degradation perception and couples residual degradation filtering with complementary cross-modal feature fusion. Extensive experiments demonstrate that our VGDCFusion significantly outperforms existing state-of-the-art fusion approaches under various degraded image scenarios. Our code is available at https://github.com/Lmmh058/VGDCFusion.

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