论文标题
光滑的随机密度电场重建
Smooth stochastic density field reconstruction
论文作者
论文摘要
我们介绍了一种从离散点分布（例如粒子或n体模拟的颗粒）或光谱调查中的星系中产生连续，质量和高阶的密度电场的方法。该方法包括通过在集合中每个点附近的Delaunay Tessellation施加的几何约束下扰动原始点集来生成点的实现集合。通过计算集合的平均场，我们能够显着减少由不良采样区域中的Delaunay Tessellation产生的伪影，同时保存点分布中的特征。我们的实现基于Delaunay Tessellation Field估计（DTFE）方法，但是其他镶嵌技术是可能的。此处介绍的方法具有DTFE方法的相同优势，例如自适应量表，质量保护和连续性，同时能够重建通常由基于Delaunay的方法中的人工制品主导的点分布的最微弱结构。此外，我们还提出了这种方法在图像deoising和trafact删除图像中应用的初步结果，从而突出了此处介绍的技术的广泛适用性。
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.
