ReDiffuse：用于多焦点图像融合的旋转等价扩散模型
ReDiffuse: Rotation Equivariant Diffusion Model for Multi-focus Image Fusion
AI总结：
本文提出ReDiffuse模型，通过在扩散网络中嵌入旋转等价性，解决多焦点图像融合中因模糊导致的结构失真问题，提升融合图像的结构一致性。
摘要：
扩散模型在多焦点图像融合（MFIF）中表现出色，但其应用面临挑战，因为模糊会使对称几何结构变形，导致融合图像出现意外伪影。为此，本文提出ReDiffuse，通过构建端到端的旋转等价扩散模型，确保融合结果忠实保留输入图像的原始方向和结构一致性。通过四个数据集的全面评估，ReDiffuse在六个评估指标上实现了2.8%-6.64%的提升。
英文摘要：
Diffusion models have achieved impressive performance on multi-focus image fusion (MFIF). However, a key challenge in applying diffusion models to the ill-posed MFIF problem is that defocus blur can make common symmetric geometric structures (e.g., textures and edges) appear warped and deformed, often leading to unexpected artifacts in the fused images. Therefore, embedding rotation equivariance into diffusion networks is essential, as it enables the fusion results to faithfully preserve the original orientation and structural consistency of geometric patterns underlying the input images. Motivated by this, we propose ReDiffuse, a rotation-equivariant diffusion model for MFIF. Specifically, we carefully construct the basic diffusion architectures to achieve end-to-end rotation equivariance. We also provide a rigorous theoretical analysis to evaluate its intrinsic equivariance error, demonstrating the validity of embedding equivariance structures. ReDiffuse is comprehensively evaluated against various MFIF methods across four datasets (Lytro, MFFW, MFI-WHU, and Road-MF). Results demonstrate that ReDiffuse achieves competitive performance, with improvements of 0.28-6.64\% across six evaluation metrics. The code is available at https://github.com/MorvanLi/ReDiffuse.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
