提出旋转等变扩散模型，解决多聚焦图像融合中的模糊失真问题
ReDiffuse: Rotation Equivariant Diffusion Model for Multi-focus Image Fusion
- 构建端到端旋转等变的扩散架构，保持几何结构方向一致性
- 在四个数据集上提升6.64%的融合质量，优于现有方法
- 适合需要精准保留边缘纹理的应用场景，如医学影像融合
扩散模型在多聚焦图像融合（MFIF）中表现优异，但模糊导致的几何形变会破坏纹理和边缘等对称结构，引发意外伪影。为此，本文提出ReDiffuse，一种旋转等变的扩散模型用于MFIF。通过精心设计基础网络结构，实现端到端旋转等变性，并提供严格的理论分析验证其内在等变误差，证明等变结构的有效性。ReDiffuse在四个数据集（Lytro、MFFW、MFI-WHU、Road-MF）上全面评估，六项指标提升0.28%-6.64%，性能领先。代码已开源。
原文摘要 · Abstract (English)
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.
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