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基于向量全变差的变分图像配准正则化。

Vectorial total variation-based regularization for variational image registration.

出版信息

IEEE Trans Image Process. 2013 Nov;22(11):4551-9. doi: 10.1109/TIP.2013.2274749. Epub 2013 Jul 24.

Abstract

To use interdependence between the primary components of the deformation field for smooth and non-smooth registration problems, the channel-by-channel total variation- or standard vectorial total variation (SVTV)-based regularization has been extended to a more flexible and efficient technique, allowing high quality regularization procedures. Based on this method, this paper proposes a fast nonlinear multigrid (NMG) method for solving the underlying Euler-Lagrange system of two coupled second-order nonlinear partial differential equations. Numerical experiments using both synthetic and realistic images not only confirm that the recommended VTV-based regularization yields better registration qualities for a wide range of applications than those of the SVTV-based regularization, but also that the proposed NMG method is fast, accurate, and reliable in delivering visually-pleasing registration results.

摘要

为了在平滑和非平滑配准问题中利用变形场的主要分量之间的相关性,基于通道的总变分或标准向量总变分(SVTV)的正则化已被扩展为一种更灵活和高效的技术,从而可以实现高质量的正则化过程。基于这种方法,本文提出了一种快速非线性多重网格(NMG)方法来求解两个耦合的二阶非线性偏微分方程组的基础欧拉-拉格朗日系统。使用合成和真实图像的数值实验不仅证实了推荐的基于 VTV 的正则化在广泛的应用中比基于 SVTV 的正则化产生更好的配准质量,而且还证实了所提出的 NMG 方法在提供视觉上令人愉悦的配准结果方面是快速、准确和可靠的。

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