Estimation for Stochastic Inverse Problems

随机逆问题出现时，一个模糊的版本， 期望的信号或图像影响随机信号的统计特性， 对测量数据进行建模的过程。 例如，量子- 体内放射性示踪剂的有限成像，成像 天文学中的伽马射线源，以及在弱光下成像 摄影. 一项联合小组研究计划正在进行中 与约翰霍普金斯大学的艾伦·卡尔一起研究新的 解决这些问题的算法。 识别算法， 高度并行的计算架构是一个重要的 研究的目的。

Stochastic inverse problems arise when a blurred version of a desired signal or image influences the statistics of a random process that models measured data. Examples include the quantum- limited imaging of radioactive tracers in the body, imaging gamma-ray sources in astronomy, and forming images in low-light photography. A joint group research program is being pursued with Alan Karr at the Johns Hopkins University to investigate new algorithms for these problems. Identifying algorithms which map onto highly parallel computational architectures is an important objective of the research.