提出高效精准的无监督多网格图像配准方法，适合有深度差异的真实场景。
EDFFDNet: Towards Accurate and Efficient Unsupervised Multi-Grid Image Registration
- 用指数衰减形变基函数实现局部化自由形变，提升效率与精度。
- 参数、内存和运行时间减少超70%、32.6%、33.7%，PSNR领先现有方法0.5 dB。
- 适合处理复杂深度场景，跨数据集泛化能力强，适用于医疗或遥感图像配准。
以往基于单仿射、多网格仿射或薄板样条的深度图像配准方法在存在深度差异的真实场景中表现受限。为此，本文提出指数衰减自由形变网络（EDFFDNet），采用具有指数衰减基函数的自由形变，具备内在局部性，显著提升效率与深度差异场景下的性能。同时引入自适应稀疏运动聚合器（ASMA），将密集交互转为稀疏，减少参数量并提升精度。此外，设计渐进式相关性精炼策略，利用全局-局部相关模式实现从粗到细的运动估计，进一步提升效率与准确性。实验表明，EDFFDNet在参数量、内存占用和总运行时上分别降低70.5%、32.6%和33.7%，相比当前最优方法提升0.5 dB PSNR；加入局部精修阶段后，EDFFDNet-2再提升1.06 dB PSNR，同时保持更低计算成本。该方法在跨数据集测试中展现强大泛化能力，优于已有深度学习方法。
原文摘要 · Abstract (English)
Previous deep image registration methods that employ single homography, multi-grid homography, or thin-plate spline often struggle with real scenes containing depth disparities due to their inherent limitations. To address this, we propose an Exponential-Decay Free-Form Deformation Network (EDFFDNet), which employs free-form deformation with an exponential-decay basis function. This design achieves higher efficiency and performs well in scenes with depth disparities, benefiting from its inherent locality. We also introduce an Adaptive Sparse Motion Aggregator (ASMA), which replaces the MLP motion aggregator used in previous methods. By transforming dense interactions into sparse ones, ASMA reduces parameters and improves accuracy. Additionally, we propose a progressive correlation refinement strategy that leverages global-local correlation patterns for coarse-to-fine motion estimation, further enhancing efficiency and accuracy. Experiments demonstrate that EDFFDNet reduces parameters, memory, and total runtime by 70.5%, 32.6%, and 33.7%, respectively, while achieving a 0.5 dB PSNR gain over the state-of-the-art method. With an additional local refinement stage,EDFFDNet-2 further improves PSNR by 1.06 dB while maintaining lower computational costs. Our method also demonstrates strong generalization ability across datasets, outperforming previous deep learning methods.
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