Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (3): 143-148.doi: 10.11896/jsjkx.190100199

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Optimization of Compressed Sensing Reconstruction Algorithms Based on Convolutional Neural Network

LIU Yu-hong,LIU Shu-ying,FU Fu-xiang   

  1. (School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)
  • Received:2019-01-24 Online:2020-03-15 Published:2020-03-30
  • About author:LIU Yu-hong,born in 1975,master,associate professor.Her main research interests include wireless communication,compressed sensing and image proces-sing.
  • Supported by:
    This work was supported by Nation Science Foundation of China (61661025, 61661026).

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Abstract: Compressed sensing theory is widely used in image and video signal processing because of its low coding complexity,resource saving and strong anti-jamming ability.However,the traditional compressed sensing technology also faces such problems as long reconstruction time,high algorithm complexity,multiple iterations and large amount of computation.Aiming at the time and quality of image reconstruction,a new convolutional neural network structure named Combine Network (CombNet) was proposed.It takes the measured values of compressed sensing as the input of convolutional neural network,connects a full connection layer,and then obtains the final output through CombNet.Experiment results show that CombNet has lower complexity and better recovery performance.At the same sampling rate,the peak signal-to-noise ratio (PSNR) of CombNet is 7.2%~13.95% higher than that of TVAL3,and 7.72%~174.84% higher than that of D-AMP.The reconstruction time of CombNe is three orders of magnitude higher than that of traditional reconstruction algorithm.When the sampling rate is very low (the sampling rate is 0.01),the average PSNR of CombNet is 11.982dB higher than D-AMP,therefore the proposed algorithm has better visual attraction.

Key words: Compressed sensing, Convolutional neural networks, PSNR, Real time reconstruction

CLC Number: 

  • TP389.1
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