Abstract

Recent developments of advanced imaging systems spur their applications in many areas, ranging from satellite remote sensing for geographic information to thermal imaging analysis for manufacturing process monitoring and control. Due to different specifications of imaging systems, the resulting image stream data (videos) have different spatial and temporal resolutions. This proposed work is based on the image stream data captured by multiple imaging systems for the same object with different but complementary spatial and temporal resolutions. For example, one system has high spatial but low temporal resolutions while the other one has opposite resolutions. The goal of this article is to develop a new super resolution method that integrates these different types of image stream data to improve both spatial and temporal resolutions, which is critical to obtaining more insightful information for more effective quality control of targeted processes or systems. To fulfill this goal, a new tensor completion model is developed by considering both smooth and sparse features simultaneously and is thus termed smooth and sparse tensor completion (SSTC). The results of the extensive case studies illustrate the superiority of our method over the elaborately selected benchmark methods.

摘要

先进成像系统的最新发展促进了它们在许多领域的应用，从用于地理信息的卫星遥感到用于制造过程监控的热成像分析。由于成像系统的规格不同，得到的图像流数据（视频）具有不同的空间和时间分辨率。这项提议的工作基于多个成像系统为具有不同但互补的空间和时间分辨率的同一物体捕获的图像流数据。例如，一个系统具有高空间分辨率但低时间分辨率，而另一个系统具有相反的分辨率。本文的目标是开发一种新的超分辨率方法，整合这些不同类型的图像流数据，以提高空间和时间分辨率，这对于获得更深入的信息以更有效地控制目标流程或系统的质量至关重要。为了实现这一目标，通过同时考虑平滑和稀疏特征开发了一种新的张量补全模型，因此被称为平滑和稀疏张量补全（SSTC）。大量案例研究的结果说明了我们的方法优于精心选择的基准方法。