DSPFusion: Image Fusion via Degradation and Semantic Dual-Prior Guidance

• URL: http://arxiv.org/abs/2503.23355v1
• Date: Sun, 30 Mar 2025 08:18:50 GMT
• Title: DSPFusion: Image Fusion via Degradation and Semantic Dual-Prior Guidance
• Authors: Linfeng Tang, Chunyu Li, Guoqing Wang, Yixuan Yuan, Jiayi Ma,
• Abstract summary: Existing fusion methods are tailored for high-quality images but struggle with degraded images captured under harsh circumstances.<n>This work presents a textbfDegradation and textbfSemantic textbfPrior dual-guided framework for degraded image textbfFusion (textbfDSPFusion)
• Score: 48.84182709640984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing fusion methods are tailored for high-quality images but struggle with degraded images captured under harsh circumstances, thus limiting the practical potential of image fusion. This work presents a \textbf{D}egradation and \textbf{S}emantic \textbf{P}rior dual-guided framework for degraded image \textbf{Fusion} (\textbf{DSPFusion}), utilizing degradation priors and high-quality scene semantic priors restored via diffusion models to guide both information recovery and fusion in a unified model. In specific, it first individually extracts modality-specific degradation priors, while jointly capturing comprehensive low-quality semantic priors. Subsequently, a diffusion model is developed to iteratively restore high-quality semantic priors in a compact latent space, enabling our method to be over $20 \times$ faster than mainstream diffusion model-based image fusion schemes. Finally, the degradation priors and high-quality semantic priors are employed to guide information enhancement and aggregation via the dual-prior guidance and prior-guided fusion modules. Extensive experiments demonstrate that DSPFusion mitigates most typical degradations while integrating complementary context with minimal computational cost, greatly broadening the application scope of image fusion.

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