论文标题
可变形的群组图像登记使用低级别和稀疏分解
Deformable Groupwise Image Registration using Low-Rank and Sparse Decomposition
论文作者
论文摘要
低级别和稀疏分解以及健壮的PCA（RPCA）是图像处理中非常成功的技术，最近在GroupWise图像注册中发现了使用。在本文中，我们研究了图像注册中最常见的rpca-dissimi \ larity指标的缺点，并得出了改进的版本。特别是，这种新的度量模型通过明确的约束而不是惩罚模型，因此避免了已建立的度量的陷阱。配备了总变化正则化，我们基于一阶原始二次优化提供了理论上合理的多级方案，以解决所得的非参数注册问题。正如数值实验所证实的那样，我们的指标特别适合于涉及对象外观和潜在稀疏扰动变化的数据。我们从数值上比较了它的同性恋与多种相关方法。
Low-rank and sparse decompositions and robust PCA (RPCA) are highly successful techniques in image processing and have recently found use in groupwise image registration. In this paper, we investigate the drawbacks of the most common RPCA-dissimi\-larity metric in image registration and derive an improved version. In particular, this new metric models low-rank requirements through explicit constraints instead of penalties and thus avoids the pitfalls of the established metric. Equipped with total variation regularization, we present a theoretically justified multilevel scheme based on first-order primal-dual optimization to solve the resulting non-parametric registration problem. As confirmed by numerical experiments, our metric especially lends itself to data involving recurring changes in object appearance and potential sparse perturbations. We numerically compare its peformance to a number of related approaches.
