A Survey on the Low-Dimensional-Model-based Electromagnetic Imaging

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 Lianlin Li, Martin Hurtado, Feng Xu, Bing Chen Zhang, Tian Jin, Tie Jun Cui, Marija Nikolic Stevanovic and Arye Nehorai (2018), “A Survey on the LowDimensional-Model-based Electromagnetic Imaging”, : Vol. 12, No. 2, pp 107–199. DOI: 10.1561/2000000103.