Paper Title:
The Study of Image Reconstruction Based on Compressed Sensing Theory
  Abstract

Compressed Sensing (Compressed Sensing, referred to as CS) is a new theory of data acquisition technology. On sparse or compressible signals, it can capture and represent the compressible signal at a rate significantly below Nyquist rate and adopt non-adaptive linear projection to keep the information and structure of original signal, and then reconstructs the original signal accurately by solving the optimizational problem. Compressed sensing breaks the bottleneck of the Shannon Theorem because it cuts down the costs of saving and transmission in data transfer. This paper briefly describes theoretical framework and the key technology of the CS theory, focuses on introducing the application in reconstructing image information of CS theory and then makes a simulation using matlab. As expected, the simulation results show that CS can reconstruct the original signal accurately under certain conditions.

  Info
Periodical
Chapter
Chapter 1: Numbers, Intelligence
Edited by
Quanjie Gao
Pages
32-35
DOI
10.4028/www.scientific.net/AMM.127.32
Citation
M. Fang, Y. M. Liu, W. Liu, H. Chen, "The Study of Image Reconstruction Based on Compressed Sensing Theory", Applied Mechanics and Materials, Vol. 127, pp. 32-35, 2012
Online since
October 2011
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$32.00
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