Particle estimation is a classical problem arising in many science fields, such as biophysics, fluid mechanics and biomedical imaging. Many interesting applications in these areas involve 3D imaging data: This work presents a technique to estimate the 3D coordinates of the center of spherical particles. This procedure has its core in the processing of the images of the scanned volume: It firstly applies denoising techniques to each frame of the scanned volume and then provides an estimation of both the center and the profile of the 2D intersections of the particles with the frames, by coupling the usage of Total Variation functional and of a regularized weighted Least Squares fit. Then, the 2D information is used to retrieve the 3D coordinates using geometrical properties. The experiments provide evidence that image denoising has a large impact on the performance of the particle tracking procedures, since they strongly depend on the quality of the initial acquisition. This work shows that the choice of tailored image denoising technique for Poisson noise leads to a better estimation of the particle positions.

粒子估计是许多科学领域出现的经典问题，例如生物物理学，流体力学和生物医学成像。这些领域中许多有趣的应用都涉及3D成像数据：这项工作提出了一种估算球形粒子中心3D坐标的技术。此过程的核心在于处理扫描体积的图像：首先将降噪技术应用于扫描体积的每个帧，然后提供粒子与帧的2D交点的中心和轮廓的估计，通过结合使用总变化函数和正则化加权最小二乘拟合。然后，将2D信息用于使用几何属性检索3D坐标。实验提供了证据，表明图像去噪对粒子跟踪程序的性能有很大影响，因为它们很大程度上取决于初始采集的质量。这项工作表明，针对泊松噪声选择量身定制的图像去噪技术可以更好地估计粒子位置。