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Shape Recovery Using Improved Fast Marching Method for SEM Image

机译:使用改进的快速行进方法对SEM图像进行形状恢复

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摘要

Fast Marching method provides a solution to the Eikonal equation, and it also can be used to solve the Shape from Shading problem. But it still has some limitations. This paper proposes a new approach to recover 3-D shape by using improved Fast Marching method. The second-order finite difference, the diagonal grid points, and new update mode is used to improved FMM and the method can recover 3-D shape for Lambert image. Then we propose a method to modify the original Scanning Electron Microscope (SEM) image with intensity modification by using affine transform and NN learning, thus the improved FMM can recover 3-D shape from SEM image. Finally, the results were compared between the proposed method and previous method. Experiment includes numerical experiments, computer simulation experiments and real image. The results show the method is satisfied with Lambert and SEM image, and both robust and accurate.
机译:快速行进方法为Eikonal方程提供了一种解决方案,它也可以用于解决“ Shading from Shading”问题。但是它仍然有一些限制。本文提出了一种使用改进的快速行进方法恢复3D形状的新方法。使用二阶有限差分,对角网格点和新的更新模式来改进FMM,该方法可以恢复Lambert图像的3D形状。然后我们提出了一种利用仿射变换和神经网络学习对图像进行强度修改的原始扫描电子显微镜图像的改进方法,从而使改进后的FMM可以从SEM图像中恢复3D形状。最后,将结果与提出的方法和以前的方法进行了比较。实验包括数值实验,计算机模拟实验和真实图像。结果表明,该方法对Lambert和SEM图像均满意,鲁棒且准确。

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