J4 ›› 2015, Vol. 32 ›› Issue (2): 144-149.

• Image and Information Proc. • Previous Articles     Next Articles

Number of Samples of Image Reconstruction Arithmetic Based On Quantum correlated Imaging

SU Feng, LIU Xiang,LONG Hua-bao, LU Shan,Yang Guang   

  1. 1 Shanghai Institute of Spacecraft Control Technology, Shanghai 200233, China; 2 Development Center of Infrared Technology, Shanghai Academy of Spaceflight Technology, Shanghai 200233, China; 3 Shanghai Key Laboratory of Aerospace Intelligent Control Technology, Shanghai 200233, China
  • Received:2014-07-08 Revised:2014-10-25 Published:2015-03-28 Online:2015-03-17

Abstract:

Quantum correlated imaging technology adopts single-point intensity detecting, huge information storage, slow imaging speed, so faster image reconstruction algorithm is required. The simulation of samples with image reconstructing algorithm is based on statistical arithmetic and compressed sensing respectively. The inputs of the compressed sensing algorithm are sparse images which are calculated with Discrete Cosine Transform (DCT) and gauss random matrices, reconstructing image with Orthogonal Matching Pursuit (OMP). The result shows that the correlated imaging algorithm based on compressed sensing can lessen the number of measurements and save data space and speed. Therefore, the study of quantum correlated imaging image reconstruction algorithm has great significance for lessening the number of samples and improving imaging speed.

Key words: quantum optics, sampling time, corrected imaging, compressed sensing, OMP