半隐式差分窄带掩模合成

Yijiang Shen, Xiaopeng Wang
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引用次数: 0

摘要

本文提出了一种距离水平集正则化掩模综合的重构方法,以保证窄带的构造简单明了,并为隐式差分格式提供了非线性扩散项。随后,仅在零水平集附近进行掩码更新,从而降低了优化维度;此外,采用半隐式离散化方法规避稳定性约束,使得步长足够大,迭代次数少,收敛性好。加性算子将掩模更新按坐标轴进行拆分,用Thomas方法求解多个相对小尺度的线性方程组。仿真结果表明,该方法克服了稳定性约束,降低了优化维数,提高了收敛性。
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Narrow-Band Mask Synthesis with Semi-Implicit Difference
In this paper, a distance level-set regularized reformulation of mask synthesis is developed to secure a simple and straightforward construction of the narrow band and provide the nonlinear diffusion term for implicit difference schemes. Subsequently, the mask update is performed only in the vicinity of the zero level set thereby reducing optimization dimensionality; moreover, the semi-implicit discretization is applied to circumvent the stability constraints enabling sufficiently large stepsize improving convergence with much less iteration numbers. Additive operator splits the mask update with respect to coordinate axes to solving multiple comparatively small scale linear systems of equations with Thomas method. Simulation results merit the superiority of the proposed approach with improved convergence by overcoming the stability constraints and reduced optimization dimensionality.
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