A Survey on the Low-Dimensional-Model-based Electromagnetic Imaging
By Lianlin Li, Peking University, China, lianlin.li@pku.edu.cn | Martin Hurtado, National University of La Plata, Argentina, martin.hurtado@ing.unlp.edu.ar | Feng Xu, Fudan University, China, fengxu@fudan.edu.cn | Bing Chen Zhang, Chinese Academy of Sciences, China, bczhang@mail.ie.ac.cn | Tian Jin, National University of Defense Technology, China, tjcui@seu.edu.cn | Tie Jun Xui, Southeast University, China, tjcui@seu.edu.cn | Marija Nikolic Stevanovic, University of Belgrade, Serbia, mnikolic@etf.rs | Arye Nehorai, Washington University in St. Louis, USA, nehorai@ese.wustl.edu
Abstract
The low-dimensional-model-based electromagnetic imaging is an emerging member of the big family of computational imaging, by which the low-dimensional models of underlying signals are incorporated into both data acquisition systems and reconstruction algorithms for electromagnetic imaging, in order to improve the imaging performance and break the bottleneck of existing electromagnetic imaging methodologies. Over the past decade, we have witnessed profound impacts of the low-dimensional models on electromagnetic imaging. However, the low-dimensional-model-based electromagnetic imaging remains at its early stage, and many important issues relevant to practical applications need to be carefully investigated. Especially, we are in the big-data era of booming electromagnetic sensing, by which massive data are being collected for retrieving very detailed information of probed objects. This survey gives a comprehensive overview on the low-dimensional models of structure signals, along with its relevant theories and low-complexity algorithms of signal recovery. Afterwards, we review the recent advancements of low-dimensional-model-based electromagnetic imaging in various applied areas. We hope this survey could bridge the gap between the model-based signal processing and the electromagnetic imaging, advance the development of low-dimensional-model-based electromagnetic imaging, and serve as a basic reference in the future research of the electromagnetic imaging across various frequency ranges.
A Survey on the Low-Dimensional-Model-based Electromagnetic Imaging
Electromagnetic imaging has been a powerful technique in various civil and military applications across medical imaging, geophysics, and space exploration. The Nyquist-Shannon theory has formed the basis for processing the signals in such systems. The advent of Compressive Sensing techniques has enabled low-dimension-model-based techniques to be used to break many of the bottlenecks of the earlier technologies.
Low-dimensional-model-based electromagnetic imaging remains at its early stage, and many important issues relevant to practical applications need to be carefully investigated. In particular, this is the era of big data with booming electromagnetic sensing, by which massive data are being collected for retrieving very detailed information of probed objects.
This monograph gives an overview of the low-dimensional models of structure signals, along with its relevant theories and low-complexity algorithms of signal recovery. It further reviews the recent advancements of low-dimensional-model-based electromagnetic imaging in various applied areas. It is a comprehensive introduction for researchers and engineers wishing to understand the state-of-the-art of electromagnetic imaging.
