OSDM-MReg: 基于一步扩散模型的多模态图像配准
OSDM-MReg: Multimodal Image Registration based One Step Diffusion Model
AI总结：
OSDM-MReg通过一步扩散模型和多模态融合策略，实现多模态图像配准的高效准确配准。
摘要：
多模态遥感图像配准旨在将不同传感器的图像对齐以实现数据融合与分析。然而，现有方法在面对大非线性辐射差异（如SAR与光学图像之间）时，往往难以提取模态不变特征。为解决这些问题，我们提出OSDM-MReg，一种新颖的多模态图像配准框架，通过图像到图像的翻译来弥合模态差距。具体而言，我们引入了一个一步未对齐的目标引导条件扩散模型（UTGOS-CDM），将源图像和目标图像转换到统一的表示域。与传统条件DDPM需要数百次迭代步骤进行推断不同，我们的模型在训练过程中引入了新的逆向翻译目标，从而在测试时能够直接预测转换后的图像，显著加快了配准过程。转换后，我们设计了多模态多尺度配准网络（MM-Reg），利用所提出的多模态融合策略提取并融合单模态和转换后的多模态图像，增强不同尺度和模态下的配准鲁棒性和精度。在OSdataset上的大量实验表明，OSDM-MReg在配准准确性方面优于现有最先进方法。
英文摘要：
Multimodal remote sensing image registration aligns images from different sensors for data fusion and analysis. However, existing methods often struggle to extract modality-invariant features when faced with large nonlinear radiometric differences, such as those between SAR and optical images. To address these challenges, we propose OSDM-MReg, a novel multimodal image registration framework that bridges the modality gap through image-to-image translation. Specifically, we introduce a one-step unaligned target-guided conditional diffusion model (UTGOS-CDM) to translate source and target images into a unified representation domain. Unlike traditional conditional DDPM that require hundreds of iterative steps for inference, our model incorporates a novel inverse translation objective during training to enable direct prediction of the translated image in a single step at test time, significantly accelerating the registration process. After translation, we design a multimodal multiscale registration network (MM-Reg) that extracts and fuses both unimodal and translated multimodal images using the proposed multimodal fusion strategy, enhancing the robustness and precision of alignment across scales and modalities. Extensive experiments on the OSdataset demonstrate that OSDM-MReg achieves superior registration accuracy compared to state-of-the-art methods.
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