Dynamic imaging addresses the recovery of a time-varying 2D or 3D object at
each time instant using its undersampled measurements. In particular, in the
case of dynamic tomography, only a single projection at a single view angle may
be available at a time, making the problem severely ill-posed. In this work, we
propose an approach, RED-PSM, which combines for the first time two powerful
techniques to address this challenging imaging problem. The first, are
partially separable models, which have been used to efficiently introduce a
low-rank prior for the spatio-temporal object. The second is the recent
Regularization by Denoising (RED), which provides a flexible framework to
exploit the impressive performance of state-of-the-art image denoising
algorithms, for various inverse problems. We propose a partially separable
objective with RED and an optimization scheme with variable splitting and ADMM,
and prove convergence of our objective to a value corresponding to a stationary
point satisfying the first order optimality conditions. Convergence is
accelerated by a particular projection-domain-based initialization. We
demonstrate the performance and computational improvements of our proposed
RED-PSM with a learned image denoiser by comparing it to a recent
deep-prior-based method TD-DIP.

通过结合部分可分离模型和 Denoising 正则化（RED），我们提出了一个优化方案，可以用于解决动态亚采样图像重建问题，并展示了与现有方法在性能和计算方面的改进。

RED-PSM: 动态成像中部分可分模型的去噪正则化

RED-PSM: Regularization by Denoising of Partially Separable Models for  Dynamic Imaging

In a dynamic social or biological environment, the interactions between the
actors can undergo large and systematic changes. In this paper we propose a
model-based approach to analyze what we will refer to as the dynamic tomography
of such time-evolving networks. Our approach offers an intuitive but powerful
tool to infer the semantic underpinnings of each actor, such as its social
roles or biological functions, underlying the observed network topologies. Our
model builds on earlier work on a mixed membership stochastic blockmodel for
static networks, and the state-space model for tracking object trajectory. It
overcomes a major limitation of many current network inference techniques,
which assume that each actor plays a unique and invariant role that accounts
for all its interactions with other actors; instead, our method models the role
of each actor as a time-evolving mixed membership vector that allows actors to
behave differently over time and carry out different roles/functions when
interacting with different peers, which is closer to reality. We present an
efficient algorithm for approximate inference and learning using our model; and
we applied our model to analyze a social network between monks (i.e., the
Sampson's network), a dynamic email communication network between the Enron
employees, and a rewiring gene interaction network of fruit fly collected
during its full life cycle. In all cases, our model reveals interesting
patterns of the dynamic roles of the actors.

本文提出了一种基于模型的方法来分析动态时间变化的网络中的演员，即动态层析。与其他网络推断技术不同，我们的方法将每个演员的角色建模为一个时间演化的混合成员向量，以更贴近现实。我们应用该模型分析了僧侣之间的社交网络，Enron 员工之间的动态电子邮件通信网络和果蝇在其完整生命周期期间重新布线的基因相互作用网络，揭示了演员动态角色的有趣模式。