Earticle

초록

영어

The block compressed sensing has brought forth the problem that the reconstructed image is of lower quality compared with that of the compressed sensing. A new method is proposed in this paper, named as Block Compressed Sensing of Self-adaptive Measurement and Combinatorial Optimization, which capably solves the problem. According to different sparsity of each image block, we firstly measure the blocks by using different projections; then, we choose measurement with the optimal reconstruction as the final measurement. Eventually, reconstruct the original image using the optimal measurement we got. The proposed method outperforms the compressed sensing in terms of real-time and better reconstruction quality is achieved than the block compressed sensing. Our experimental results verify the superiority of the proposed method.

목차

Abstract
 1. Introduction
 2. Compressed Sensing
  2.1. The Sparse Represent of Signal
  2.2. Non-linear Optimization Reconstruction
  2.3. The Irrelevant Measurement
 3. AMCO-BCS
 4. Experiments
  4.1. Experimental Environment and Index
  4.2. Experimental Procedure
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Li Mingxing [ College of Computer and Information Engneering, Beijing Technology and Business University ]
• Chen Xiuxin [ College of Computer and Information Engneering, Beijing Technology and Business University ]
• Su Weijun [ College of Computer and Information Engneering, Beijing Technology and Business University ]
• Yu Chongchong [ College of Computer and Information Engneering, Beijing Technology and Business University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605