光滑随机密度场重建
Smooth stochastic density field reconstruction
摘要：
我们介绍了一种方法，用于产生一个连续的，质量守恒和高阶可微的密度场从离散点分布，如粒子或晕从N体模拟或星系从光谱调查。该方法包括生成一个合奏的点实现扰动的原始点集以下的Delaunay镶嵌在附近的每个点的集合中施加的几何约束。通过计算平均场的合奏，我们能够显着减少文物所产生的Delaunay镶嵌在采样差的地区，同时保存点分布的功能。我们的实现是基于Delaunay曲面细分场估计（DTFE）方法，但其他曲面细分技术是可能的。这里提出的方法具有DTFE方法的相同优点，如自适应尺度，质量守恒和连续性，同时能够重建即使是最微弱的点分布结构通常占主导地位的工件在基于Delaunay的方法。此外，我们还提出了这种方法的应用程序的初步结果，图像去噪和伪影去除，突出了这里介绍的技术的广泛适用性。
英文摘要：
We introduce a method for generating a continuous, mass-conserving and high-order differentiable density field from a discrete point distribution such as particles or halos from an N-body simulation or galaxies from a spectroscopic survey. The method consists on generating an ensemble of point realizations by perturbing the original point set following the geometric constraints imposed by the Delaunay tessellation in the vicinity of each point in the set. By computing the mean field of the ensemble we are able to significantly reduce artifacts arising from the Delaunay tessellation in poorly sampled regions while conserving the features in the point distribution. Our implementation is based on the Delaunay Tessellation Field Estimation (DTFE) method, however other tessellation techniques are possible. The method presented here shares the same advantages of the DTFE method such as self-adaptive scale, mass conservation and continuity, while being able to reconstruct even the faintest structures of the point distribution usually dominated by artifacts in Delaunay-based methods. Additionally, we also present preliminary results of an application of this method to image denoising and artifact removal, highlighting the broad applicability of the technique introduced here.
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