Machine-learning reprogrammable metasurface imager

Conventional microwave imagers usually require either time-consuming data acquisition, or complicated reconstruction algorithms for data post-processing, making them largely ineffective for complex in-situ sensing and monitoring. Here, we experimentally report a real-time digital-metasurface imager that can be trained in-situ to generate the radiation patterns required by machine-learning optimized measurement modes. This imager is electronically reprogrammed in real time to access the optimized solution for an entire data set, realizing storage and transfer of full-resolution raw data in dynamically varying scenes. High-accuracy image coding and recognition are demonstrated in situ for various image sets, including hand-written digits and through-wall body gestures, using a single physical hardware imager, reprogrammed in real time. Our electronically controlled metasurface imager opens new venues for intelligent surveillance, fast data acquisition and processing, imaging at various frequencies, and beyond.Conventional imagers require time-consuming data acquisition, or complicated reconstruction algorithms for data post-processing. Here, the authors demonstrate a real-time digital-metasurface imager that can be trained in-situ to show high accuracy image coding and recognition for various image sets.

[1]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[2]  I. Jolliffe.  Principal Component Analysis , 2002 .

[3]  Mark A Neifeld,et al.  Feature-specific imaging. , 2003, Applied optics.

[4]  Shree K. Nayar,et al.  Programmable Imaging: Towards a Flexible Camera , 2006, International Journal of Computer Vision.

[5]  Mark A Neifeld,et al.  Face recognition by using feature-specific imaging. , 2005, Applied optics.

[6]  Wai Lam Chan,et al.  A single-pixel terahertz imaging system based on compressed sensing , 2008 .

[7]  H. Atwater,et al.  Frequency tunable near-infrared metamaterials based on VO2 phase transition. , 2009, Optics express.

[8]  Nikolay I. Zheludev,et al.  Reconfigurable photonic metamaterials , 2011, CLEO: 2011 - Laser Science to Photonic Applications.

[9]  N. Yu,et al.  Light Propagation with Phase Discontinuities: Generalized Laws of Reflection and Refraction , 2011, Science.

[10]  Nathan Halko,et al.  Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions , 2009, SIAM Rev..

[11]  Andreas Tünnermann,et al.  Spatial and Spectral Light Shaping with Metamaterials , 2012, Advanced materials.

[12]  A. Kildishev,et al.  Broadband Light Bending with Plasmonic Nanoantennas , 2012, Science.

[13]  Federico Capasso,et al.  Holographic detection of the orbital angular momentum of light with plasmonic photodiodes , 2012, Nature Communications.

[14]  David R. Smith,et al.  Infrared metamaterial phase holograms. , 2012, Nature materials.

[15]  Kan Yao,et al.  Generalized laws of reflection and refraction from transformation optics , 2012, 1202.5829.

[16]  Jing Kong,et al.  Broad electrical tuning of graphene-loaded plasmonic antennas. , 2013, Nano letters.

[17]  Vladimir M. Shalaev,et al.  Metasurface holograms for visible light , 2013, Nature Communications.

[18]  David R. Smith,et al.  Metamaterial Apertures for Computational Imaging , 2013, Science.

[19]  Eric Plum,et al.  An electromechanically reconfigurable plasmonic metamaterial operating in the near-infrared. , 2013, Nature nanotechnology.

[20]  E. Hasman,et al.  Spin-Optical Metamaterial Route to Spin-Controlled Photonics , 2013, Science.

[21]  Qiang Cheng,et al.  Coding metamaterials, digital metamaterials and programmable metamaterials , 2014, Light: Science & Applications.

[22]  Guoxing Zheng,et al.  Metasurface holograms reaching 80% efficiency. , 2015, Nature nanotechnology.

[23]  Jacob Scheuer,et al.  Dynamically controlled plasmonic nano-antenna phased array utilizing vanadium dioxide [Invited] , 2015 .

[24]  D. Tsai,et al.  Gate-Tunable Conducting Oxide Metasurfaces. , 2015, Nano letters.

[25]  Lianlin Li,et al.  Single-shot and single-sensor high/super-resolution microwave imaging based on metasurface , 2016, Scientific Reports.

[26]  Xiang Wan,et al.  Transmission-Type 2-Bit Programmable Metasurface for Single-Sensor and Single-Frequency Microwave Imaging , 2016, Scientific Reports.

[27]  Pavan K. Turaga,et al.  Reconstruction-Free Action Inference from Compressive Imagers , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Aswin C. Sankaranarayanan,et al.  Enhanced Compressive Imaging Using Model-Based Acquisition: Smarter sampling by incorporating domain knowledge , 2016, IEEE Signal Processing Magazine.

[29]  David R. Smith,et al.  Spatially resolving antenna arrays using frequency diversity. , 2016, Journal of the Optical Society of America. A, Optics, image science, and vision.

[30]  Dennis W. Prather,et al.  Computational Millimeter Wave Imaging: Problems, progress, and prospects , 2016, IEEE Signal Processing Magazine.

[31]  J. Teng,et al.  Optically reconfigurable metasurfaces and photonic devices based on phase change materials , 2015, Nature Photonics.

[32]  Maokun Li,et al.  A programmable metasurface with dynamic polarization, scattering and focusing control , 2016, Scientific Reports.

[33]  J. Romberg,et al.  Terahertz time-gated spectral imaging for content extraction through layered structures , 2016, Nature Communications.

[34]  Guoxing Zheng,et al.  Single-pixel computational ghost imaging with helicity-dependent metasurface hologram , 2017, Science Advances.

[35]  Shuang Zhang,et al.  Electromagnetic reprogrammable coding-metasurface holograms , 2017, Nature Communications.

[36]  David R. Smith,et al.  Large Metasurface Aperture for Millimeter Wave Computational Imaging at the Human-Scale , 2017, Scientific Reports.

[37]  Thomas Fromenteze,et al.  Computational polarimetric microwave imaging. , 2017, Optics express.