基于PDE的自适应鲁向异性图像配准方法研究

doi:10.3969/j.issn.1000-436x.2013.05.022

Abstract

Abstract:

A PDE-based adaptive anisotropic model for image registration was proposed to solve the problem that tradi-tional optical flow brings on the image blurring and details losing.The new model introduces adaptivity image registration,and defines anisotropic diffusion function with the ability of preserving image structure as the regu-larization term;the data term use a non-quadratic penalty function with the assumption in brightness constant to improve the robust of the model.The experimental results show that the model can efficiently protect the image structure and achieve accurate registration of the complex image like brain.

Key words: optical flow, image registration, adaptive, anisotropic, edge alignment

Shu-jing JIANG,Bao-qin HEI,Jiu-xing ZHANG,Qian-nan LI. Research on PDE-based adaptive anisotropic image registration[J]. Journal on Communications, 2013, 34(5): 192-199.

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特征	梯度	曲率	α	β
边缘	较大	较小	α→ 1	β→ 0
尖角	较大	较大	α→ 0	β→ 0
噪声	较小	较大	α→ 1	β→ 1
平滑区域	较小	较小	α→ 1	β→ 1

模型	Mean	Var	PSNR	AM '
Horn模型	?0.10	30.54	33.28	0.164 5
Lucas-Kanade模型	1.59	38.36	32.29	0.154 1
本文模型	?0.08	9.17	38.51	0.943 9

模型	图像大小	迭代次数	计算时间/s
Horn模型	257× 221	30	1.303 8
Lucas-Kanade模型	257× 221	窗口大小为5,5层迭代	36.903 9
本文模型	257× 221	30	15.581 6

模型	Mean	Var	PSNR	AM '
Horn模型	?4.68	35.57	32.62	0.028 1
Lucas-Kanade模型	10.05	37.91	32.34	0.526 8
本文模型	1.21	9.17	36.23	0.908 9

模型	图像大小	迭代次数	计算时间/s
Horn模型	100×100	20	0.865 4
Lucas-Kanade模型	100×100	窗口大小为5,5层迭代	7.276 7
本文模型	100×100	20	2.764 8

Research on PDE-based adaptive anisotropic image registration

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