earticle

논문검색

Measurement Matrix Construction Algorithm for Compressed Sensing based on QC-LDPC Matrix

원문정보

초록

영어

The measurement matrix of compressed sensing has a significant impact for sampling and reconstruction algorithm of the original signal. At present, the majority of the measurement matrix is randomly constructed, and it is difficulty for hardware implementation in the practical applications. In this paper, we use the sparse characteristic of parity- check matrix of LDPC codes, construct measurement matrix based on QC-LDPC (Quasi-cyclic low-density parity-check) matrix, which is a structural and sparse deterministic measurement matrix. The simulation results show that, the measurement matrix is proposed in this paper not only can obtain a better reconstructed image quality, but also it can reduce the complexity of hardware implementation for quasi-cyclic.

목차

Abstract
 1. Introduction
 2. The Construction of Measurement Matrix
  2.1 Restricted Isometry Property
  2.2 The Parity Check Matrix of LDPC
  2.3 The Parity Check Matrix of QC-LDPC
  2.4 Design of QC-LDPC Matrix based on PEG Algorithm
 3. The Simulation Results and Discussions
  3.1 The Simulation and Analysis of One-Dimensional Signal
  3.2 The Simulation and Analysis of Two-Dimensional Signal
 4. Conclusion
 Acknowledgement
 References

저자정보

  • NIE Yang Department of Physics, Jining Normal University, Inner Mongolia, China, Digital Engineering Center, Communication University of China, Beijing, China
  • JING Li-li Department of Physics, Jining Normal University, Inner Mongolia, China

참고문헌

자료제공 : 네이버학술정보

    ※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

    0개의 논문이 장바구니에 담겼습니다.