Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 188-191.doi: 10.11896/jsjkx.200200058

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Ghost Imaging Reconstruction Algorithm Based on Block Sparse Bayesian Model

WU Xue-lin1, ZHU Rong1,2, GUO Ying3   

  1. 1 School of Internet of Things Engineering,Wuxi Taihu University,Wuxi,Jiangsu 214000,China
    2 School of Computer Science,Qufu Normal University,Rizhao,Shandong 276826,China
    3 School of Automation,Central South University,Changsha 410083,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:WU Xue-lin,born in 1981,postgra-duate.Her main research interests include network security and so on.
    ZHU Rong,born in 1975,Ph.D,asso-ciate professor,is a member of China Computer Federation.Her main research interests include image proces-sing,images reconstruction,machine learning and bioinformatics.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61876407) and Jiangsu Key Construction Laboratory of IoT Application Technology(19WXWL05,18WXWL01).

Abstract: Conventional camera systems use light transmitted or backscattered from an object to form an image on a film or focal plane detector array.Ghost imaging systems utilize the spatial correlation between separated light fields to obtain images without recording the images themselves,and have great application potential in remote sensing,medical,and microscopic imaging.A ghost imaging reconstruction algorithm based on block sparse Bayesian model is proposed to improve the problem that large-scale image reconstruction storage is difficult to achieve in traditional ghost imaging systems.This algorithm divides a large-size target image into several small-sized image blocks of the same size.Based on the Bayesian learning model,each image block is subjected to compressed sensing reconstruction.Subsequently,the reconstruction result of each image block is merged,resulting in the final target reconstructed image.The simulation results show that the image quality of the reconstructed image can be improved from the block sparse Bayesian ghost imaging reconstruction algorithm,and the large-size target image can be reconstructed effectively under the traditional computer configuration for practical implementations.

Key words: Block sparse Bayesian model, Compressed sensing, Ghost imaging, Images reconstruction

CLC Number: 

  • TP13
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