Convolutional neural network based radio frequency tomographic reconstruction is explored in this study. Due to the limited amount of sensors used in RF tomographic imaging system, analytical reconstruction such as filtered back-projection usually generates strong artifacts in the reconstructed images. The state of art algebraic reconstruction methods use prior knowledge to regularize reconstruction and reduce artifacts, but suffer from high computational complexity. Our study shows reconstruction based on convolutional neural network, a learning based approach, is effective in removing artifacts caused by limited number of sensors, and has low computational cost, which makes it suitable for real-time applications.


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    Title :

    Radio Frequency Tomographic Reconstruction Based on Convolutional Neural Networks


    Contributors:
    Li, Jia (author) / Ewing, Robert L. (author) / Shen, Xiaoping (author)


    Publication date :

    2018-07-01


    Size :

    1413610 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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