Biograph: Prof. Yiming Zhu, graduated from the University of Tokyo with a Ph.D. degree, is a National special support program for high-level personnel recruitment (Ten-thousand Talents Program) “Youth Science and technology innovation leader” and “Young Yangtze Professor” in the University of Shanghai for Science and Technology, and currently serves as a vice director of the Shanghai Key Lab of Modern Optical System. He is also the “The national key talent project”, “Outstanding Youth Foundation”, and “Special State Council allowance” winner. He is currently the director of the Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, the Terahertz Precision Biomedical Technology Overseas Expertise Introduction Center for Discipline Innovation, the Terahertz Technology Innovation International Joint Laboratory at University of Shanghai for Science and Technology and Lomonosov Moscow State University.
His research focuses on terahertz technologies and applications, including terahertz devices, terahertz spectroscopy, imaging systems, terahertz bio-applications, etc. Up to now, he has published more than 100 papers, including >40 papers in Light Sci. & Appl., Adv. Opt. Mater., Appl. Phys. Lett., Opt. Lett., Opt. Exp., et al (Top 5%), including 5 papers are selected as ESI papers.
Title: THz 3-D SAR Sparse Imaging with 2-D Pseudo-random Array
Abstract: For real-time Terahertz (THz) three-dimensional (3-D) imaging, spatially 2-D sparse sampling is one of the most promising techniques to reduce the heavy complexity. We propose a new THz 3-D synthetic-aperture-radar (SAR) sparse imaging method by establishing a 2-D pseudorandom array. The proposed method consists of sparse sampling-pattern design and sparse-imaging processing, to answer what the under-sampling pattern is and how much the number of samples can be thinned by the current sparse imaging. The constant, showing the extent to which our proposed method can reduce the number of samples is numerically estimated through simulations. The imaging results obtained by the numerical and experimental simulations demonstrate that our proposed method can offer performance comparable to the dense sampling by Nyquist law, even when the sparse degree is thinned to 30%.