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    End-to-End Video Compressive Sensing Using Anderson-Accelerated Unrolled Networks

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    Description:
    Accepted manuscript
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    Type
    Conference Paper
    Authors
    Li, Yuqi
    Qi, Miao cc
    Gulve, Rahul
    Wei, Mian
    Genov, Roman
    Kutulakos, Kiriakos N.
    Heidrich, Wolfgang cc
    KAUST Department
    Computational Imaging Group
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering
    Visual Computing Center (VCC)
    Date
    2020-06-02
    Online Publication Date
    2020-06-02
    Print Publication Date
    2020-04
    Permanent link to this record
    http://hdl.handle.net/10754/663826
    
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    Abstract
    Compressive imaging systems with spatial-temporal encoding can be used to capture and reconstruct fast-moving objects. The imaging quality highly depends on the choice of encoding masks and reconstruction methods. In this paper, we present a new network architecture to jointly design the encoding masks and the reconstruction method for compressive high-frame-rate imaging. Unlike previous works, the proposed method takes full advantage of denoising prior to provide a promising frame reconstruction. The network is also flexible enough to optimize full-resolution masks and efficient at reconstructing frames. To this end, we develop a new dense network architecture that embeds Anderson acceleration, known from numerical optimization, directly into the neural network architecture. Our experiments show the optimized masks and the dense accelerated network respectively achieve 1.5 dB and 1 dB improvements in PSNR without adding training parameters. The proposed method outperforms other state-of-the-art methods both in simulations and on real hardware. In addition, we set up a coded two-bucket camera for compressive high-frame-rate imaging, which is robust to imaging noise and provides promising results when recovering nearly 1,000 frames per second.
    Citation
    Li, Y., Qi, M., Gulve, R., Wei, M., Genov, R., Kutulakos, K. N., & Heidrich, W. (2020). End-to-End Video Compressive Sensing Using Anderson-Accelerated Unrolled Networks. 2020 IEEE International Conference on Computational Photography (ICCP). doi:10.1109/iccp48838.2020.9105237
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Conference/Event name
    2020 IEEE International Conference on Computational Photography (ICCP)
    ISBN
    978-1-7281-5231-8
    DOI
    10.1109/ICCP48838.2020.9105237
    Additional Links
    https://ieeexplore.ieee.org/document/9105237/
    https://ieeexplore.ieee.org/document/9105237/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9105237
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICCP48838.2020.9105237
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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