Dual-Domain Perspective on Degradation-Aware Fusion: A VLM-Guided Robust Infrared and Visible Image Fusion Framework

• URL: http://arxiv.org/abs/2509.05000v1
• Date: Fri, 05 Sep 2025 10:48:46 GMT
• Title: Dual-Domain Perspective on Degradation-Aware Fusion: A VLM-Guided Robust Infrared and Visible Image Fusion Framework
• Authors: Tianpei Zhang, Jufeng Zhao, Yiming Zhu, Guangmang Cui,
• Abstract summary: GD2Fusion is a novel framework that integrates vision-language models for degradation perception with dual-domain (frequency/spatial) joint optimization.<n>It achieves superior fusion performance compared with existing algorithms and strategies in dual-source degraded scenarios.
• Score: 9.915632806109555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most existing infrared-visible image fusion (IVIF) methods assume high-quality inputs, and therefore struggle to handle dual-source degraded scenarios, typically requiring manual selection and sequential application of multiple pre-enhancement steps. This decoupled pre-enhancement-to-fusion pipeline inevitably leads to error accumulation and performance degradation. To overcome these limitations, we propose Guided Dual-Domain Fusion (GD^2Fusion), a novel framework that synergistically integrates vision-language models (VLMs) for degradation perception with dual-domain (frequency/spatial) joint optimization. Concretely, the designed Guided Frequency Modality-Specific Extraction (GFMSE) module performs frequency-domain degradation perception and suppression and discriminatively extracts fusion-relevant sub-band features. Meanwhile, the Guided Spatial Modality-Aggregated Fusion (GSMAF) module carries out cross-modal degradation filtering and adaptive multi-source feature aggregation in the spatial domain to enhance modality complementarity and structural consistency. Extensive qualitative and quantitative experiments demonstrate that GD^2Fusion achieves superior fusion performance compared with existing algorithms and strategies in dual-source degraded scenarios. The code will be publicly released after acceptance of this paper.

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