何凯, 魏颖, 王阳, 黄婉蓉. 一种改进的非刚性图像配准算法[J]. 工程科学学报, 2019, 41(7): 955-960. DOI: 10.13374/j.issn2095-9389.2019.07.015
引用本文: 何凯, 魏颖, 王阳, 黄婉蓉. 一种改进的非刚性图像配准算法[J]. 工程科学学报, 2019, 41(7): 955-960. DOI: 10.13374/j.issn2095-9389.2019.07.015
HE Kai, WEI Ying, WANG Yang, HUANG Wan-rong. An improved non-rigid image registration approach[J]. Chinese Journal of Engineering, 2019, 41(7): 955-960. DOI: 10.13374/j.issn2095-9389.2019.07.015
Citation: HE Kai, WEI Ying, WANG Yang, HUANG Wan-rong. An improved non-rigid image registration approach[J]. Chinese Journal of Engineering, 2019, 41(7): 955-960. DOI: 10.13374/j.issn2095-9389.2019.07.015

一种改进的非刚性图像配准算法

An improved non-rigid image registration approach

  • 摘要: 非刚性图像配准一直是计算机视觉领域的研究重点. 为解决上述问题, 提出一种改进的光流场模型算法, 以提高光流估计的准确度. 算法首先对原始变分光流模型进行了改进, 提出利用新的各向异性正则项来代替原来的同向扩散函数, 以避免图像模糊, 保留图像的边缘特征与细节特征; 此外, 通过引入包含邻域信息的非局部平滑项来去除光流噪点, 同时增加了一个结合图像结构与光流运动信息的权函数, 以减少过平滑所造成的细节丢失, 提高算法的鲁棒性. 最后, 利用交替最小化与金字塔分层迭代策略相结合的方法求解位移场, 实现非刚性图像的自动配准. 仿真实验结果表明, 与传统方法相比, 本文算法对不同类型的非刚性图像均具有较高的鲁棒性, 取得了理想的图像配准效果.

     

    Abstract: With the rapid development of image registration technology, it is being widely used in the fields of medical image processing, remote sensing image analysis, computer vision, and others. Image registration involves two or more images that contain the same object that are obtained under different conditions. Geometric mapping between images is realized by spatial geometric transformation, so that the points in one image can be related to their corresponding points in the other. Compared with rigid transformations, non-rigid transformations usually have severe local distortions and obvious nonlinear characteristics. So, it is difficult to describe non-rigid transformations using a unified transformation model. For this reason, non-rigid image registration has always been an issue and a source of difficulty in the field of computer vision. To solve this problem, an improved optical-flow-model algorithm was proposed to more accurately estimate the optical flow field. First, the original variational optical flow model was improved. To prevent blurring and preserve the edge and detail features of images, a new anisotropic regular term was proposed to replace the original homologous diffusion term. Then, to remove optical flow outliers, a non-local smoothness term was introduced that contained neighborhood information. Moreover, a weight function that combines image-structure and optical-flow information was added to reduce the loss of detail caused by over-smoothing and to improve robustness. Finally, to solve the displacement field and realize the automatic registration of non-rigid images, an alternating minimization method and pyramid hierarchical iteration strategy were utilized. To verify the effectiveness of the proposed algorithm, subjective and objective evaluation values such as the peak signal-to-noise ratio (PSNR) and normalized mutual information (NMI) were adopted to analyze the registration results. Compared with state-of-the-art methods, experimental results reveal the robustness and ideal registration effects of the proposed method on different types of non-rigid images.

     

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