@Article{jiot.2020.09116,
AUTHOR = {Caifeng Cheng, Deshu Lin},
TITLE = {Based on Compressed Sensing of Orthogonal Matching Pursuit Algorithm  Image Recovery},
JOURNAL = {Journal on Internet of Things},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {37--45},
URL = {http://www.techscience.com/jiot/v2n1/39679},
ISSN = {2579-0080},
ABSTRACT = {Compressive sensing theory mainly includes the sparsely of signal 
processing, the structure of the measurement matrix and reconstruction 
algorithm. Reconstruction algorithm is the core content of CS theory, that is, 
through the low dimensional sparse signal recovers the original signal accurately. 
This thesis based on the theory of CS to study further on seismic data 
reconstruction algorithm. We select orthogonal matching pursuit algorithm as a 
base reconstruction algorithm. Then do the specific research for the 
implementation principle, the structure of the algorithm of AOMP and make the 
signal simulation at the same time. In view of the OMP algorithm reconstruction 
speed is slow and the problems need to be a given number of iterations, which 
developed an improved scheme. We combine the optimized OMP algorithm of 
constraint the optimal matching of item selection strategy, the backwards 
gradient projection ideas of adaptive variance step gradient projection method 
and the original algorithm to improve it. Simulation experiments show that 
improved OMP algorithm is superior to traditional OMP algorithm of 
improvement in the reconstruction time and effect under the same condition. 
This paper introduces CS and most mature compressive sensing algorithm at 
present orthogonal matching pursuit algorithm. Through the program design 
realize basic orthogonal matching pursuit algorithms, and design realize basic 
orthogonal matching pursuit algorithm of one-dimensional, two-dimensional 
signal processing simulation.},
DOI = {10.32604/jiot.2020.09116}
}