PCReg-Net：渐进式对比引导的跨域图像对齐
PCReg-Net: Progressive Contrast-Guided Registration for Cross-Domain Image Alignment
AI总结：
PCReg-Net通过渐进式对比引导方法实现跨域图像对齐，采用四个轻量级模块提高配准精度与效率，达到实时推断性能。
摘要：
跨异质域的可变形图像配准仍然具有挑战性，因为耦合的外观变化和几何错位违反了传统方法所依赖的亮度恒定性假设。我们提出了PCReg-Net，一种渐进式对比引导的配准框架，通过四个轻量级模块进行由粗到细的对齐：（1）一个用于初始粗对齐的配准U-Net，（2）一个参考特征提取器，从固定图像中捕捉多尺度的结构线索，（3）一个多尺度对比模块，通过比较粗配准和参考特征来识别残余错位，（4）一个具有特征注入的精修U-Net，产生最终的高质量输出。我们在FIRE-Reg-256视网膜底片基准上进行了评估，证明了其在传统方法和深度学习基线上的改进。此外，在两个显微镜基准上的额外实验进一步证实了跨域应用的可行性。仅使用256万个参数，PCReg-Net在141 FPS下实现实时推断。代码可在https://github.com/JiahaoQin/PCReg-Net上获取。
英文摘要：
Deformable image registration across heterogeneous domains remains challenging because coupled appearance variation and geometric misalignment violate the brightness constancy assumption underlying conventional methods. We propose PCReg-Net, a progressive contrast-guided registration framework that performs coarse-to-fine alignment through four lightweight modules: (1)~a registration U-Net for initial coarse alignment, (2)~a reference feature extractor capturing multi-scale structural cues from the fixed image, (3)~a multi-scale contrast module that identifies residual misalignment by comparing coarse-registered and reference features, and (4)~a refinement U-Net with feature injection that produces the final high-fidelity output. We evaluate on the FIRE-Reg-256 retinal fundus benchmark, demonstrating improvements over both traditional and deep learning baselines. Additional experiments on two microscopy benchmarks further confirm cross-domain applicability. With only 2.56M parameters, PCReg-Net achieves real-time inference at 141 FPS. Code is available at https://github.com/JiahaoQin/PCReg-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。
