无形变形的跨域图像配准 via 位置编码时间注意力
Deformation-Free Cross-Domain Image Registration via Position-Encoded Temporal Attention
AI总结：
GPEReg-Net通过位置编码时间注意力机制实现无变形跨域图像配准，取得最佳性能并运行更快。
摘要：
我们解决了跨域图像配准问题，其中配对图像表现出耦合的几何错位和领域特定的外观偏移。我们将此问题形式化为一个分解问题：将每张图像分解为领域不变的场景表示和全局外观统计量，使得配准简化为通过自适应实例归一化（AdaIN）重新组合移动图像的场景结构与固定图像的外观。这种分解消除了显式变形场估计的需要。为了利用连续采集中的时间一致性，我们引入了位置编码的跨帧注意力机制，该机制融合了可学习和正弦位置嵌入，并在滑动窗口的邻帧上使用多头注意力，丰富场景表示与帧间上下文。我们将此框架实例化为GPEReg-Net，并在两个基准上进行评估：FIRE-Reg-256（视网膜底片，半刚性）和HPatches-Reg-256（合成纹理块，仿射）。GPEReg-Net在两个基准上均取得最佳性能（FIRE：SSIM=0.928，PSNR=33.47 dB；HPatches：SSIM=0.450，PSNR=21.01 dB），超越了所有基线，包括基于变形的方法，同时比SAS-Net快1.87倍。代码：https://github.com/JiahaoQin/GPEReg-Net。
英文摘要：
We address the problem of cross-domain image registration, where paired images exhibit coupled geometric misalignment and domain-specific appearance shift. We formalize this as a factorization problem: decomposing each image into a domain-invariant scene representation and a global appearance statistic, such that registration reduces to recombining the scene structure of the moving image with the appearance of the fixed image via Adaptive Instance Normalization (AdaIN). This factorization eliminates the need for explicit deformation field estimation. To exploit temporal coherence in sequential acquisitions, we introduce a position-encoded cross-frame attention mechanism that fuses learnable and sinusoidal position embeddings with multi-head attention over a sliding window of neighboring frames, enriching the scene representation with inter-frame context. We instantiate this framework as GPEReg-Net and evaluate on two benchmarks: FIRE-Reg-256 (retinal fundus, semi-rigid) and HPatches-Reg-256 (synthetic textured patches, affine). GPEReg-Net achieves state-of-the-art performance on both benchmarks (FIRE: SSIM = 0.928, PSNR = 33.47 dB; HPatches: SSIM = 0.450, PSNR = 21.01 dB), surpassing all baselines, including deformation-based methods, while running 1.87x faster than SAS-Net. Code: https://github.com/JiahaoQin/GPEReg-Net.
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。
