This paper presents a novel method for the reconstruction of high-resolution
temporal images in dynamic tomographic imaging, particularly for discrete
objects with smooth boundaries that vary over time. Addressing the challenge of
limited measurements per time point, we propose a technique that
synergistically incorporates spatial and temporal information of the dynamic
objects. This is achieved through the application of the level-set method for
image segmentation and the representation of motion via a sinusoidal basis. The
result is a computationally efficient and easily optimizable variational
framework that enables the reconstruction of high-quality 2D or 3D image
sequences with a single projection per frame. Compared to current methods, our
proposed approach demonstrates superior performance on both synthetic and
pseudo-dynamic real X-ray tomography datasets. The implications of this
research extend to improved visualization and analysis of dynamic processes in
tomographic imaging, finding potential applications in diverse scientific and
industrial domains.

提出一种新方法，通过结合空间和时间信息，采用水平集方法和正弦基函数表达动态物体的运动，实现高分辨率时间图像的重建。与当前方法相比，在合成和伪动态真实 X 射线断层扫描数据集上表现出优越性能，为断层显像的动态过程提供改进的可视化和分析。

离散动态物体的单次成像

Single-shot Tomography of Discrete Dynamic Objects

Cities around the world face a critical shortage of affordable and decent
housing. Despite its critical importance for policy, our ability to effectively
monitor and track progress in urban housing is limited. Deep learning-based
computer vision methods applied to street-level images have been successful in
the measurement of socioeconomic and environmental inequalities but did not
fully utilize temporal images to track urban change as time-varying labels are
often unavailable. We used self-supervised methods to measure change in London
using 15 million street images taken between 2008 and 2021. Our novel
adaptation of Barlow Twins, Street2Vec, embeds urban structure while being
invariant to seasonal and daily changes without manual annotations. It
outperformed generic embeddings, successfully identified point-level change in
London's housing supply from street-level images, and distinguished between
major and minor change. This capability can provide timely information for
urban planning and policy decisions toward more liveable, equitable, and
sustainable cities.

利用基于深度学习的计算机视觉方法，该研究使用 2008 年至 2021 年间的 1500 万街道图像来测量伦敦的变化，成功地识别了住房供应的变化，为城市规划和政策决策提供及时信息，以建设更宜居、公平和可持续发展的城市。