Image Deformation Estimation via Multi-Objective Optimization
Image Deformation Estimation via Multi-Objective Optimization
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DOI：
10.1109/access.2022.3174360
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发表时间：
2021-06
期刊：
影响因子：
3.9
通讯作者：
Takumi Nakane;Haoran Xie;Chao Zhang
中科院分区：
文献类型：
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作者：
Takumi Nakane;Haoran Xie;Chao Zhang
自由变形模型可以通过在图像上操纵控制点晶格来表示广泛的非刚性变形。然而，由于大量的参数，这是具有挑战性的自由形式的变形模型直接适合变形图像的变形估计，因为适应度景观的复杂性。在本文中，我们铸造的配准任务作为一个多目标优化问题（MOP）的事实，每个控制点的影响区域相互重叠。具体地说，通过将模板图像划分为几个区域，并独立地测量每个区域的相似性，多个目标的建立和变形估计，从而可以实现通过解决与现成的多目标进化算法（MOEAs）的MOP。此外，采用图像金字塔与控制点网格细分相结合的方法实现了由粗到精的策略。具体来说，当前图像层的优化候选解被下一层继承，这增加了处理大变形的能力。此外，后处理过程中提出了一个单一的输出，利用帕累托最优解。合成和真实世界的图像上的对比实验表明，我们的变形估计方法的有效性和实用性。
The free-form deformation model can represent a wide range of non-rigid deformations by manipulating a control point lattice over the image. However, due to a large number of parameters, it is challenging to fit the free-form deformation model directly to the deformed image for deformation estimation because of the complexity of the fitness landscape. In this paper, we cast the registration task as a multi-objective optimization problem (MOP) according to the fact that regions affected by each control point overlap with each other. Specifically, by partitioning the template image into several regions and measuring the similarity of each region independently, multiple objectives are built and deformation estimation can thus be realized by solving the MOP with off-the-shelf multi-objective evolutionary algorithms (MOEAs). In addition, a coarse-to-fine strategy is realized by image pyramid combined with control point mesh subdivision. Specifically, the optimized candidate solutions of the current image level are inherited by the next level, which increases the ability to deal with large deformation. Also, a post-processing procedure is proposed to generate a single output utilizing the Pareto optimal solutions. Comparative experiments on both synthetic and real-world images show the effectiveness and usefulness of our deformation estimation method.