@Article{jiot.2020.09117,
AUTHOR = {Caifeng Cheng, Deshu Lin},
TITLE = {Image Reconstruction Based on Compressed Sensing Measurement Matrix  Optimization Method},
JOURNAL = {Journal on Internet of Things},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {47--54},
URL = {http://www.techscience.com/jiot/v2n1/39680},
ISSN = {2579-0080},
ABSTRACT = {In this paper, the observation matrix and reconstruction algorithm of 
compressed sensing sampling theorem are studied. The advantages and 
disadvantages of greedy reconstruction algorithm are analyzed. The 
disadvantages of signal sparsely are preset in this algorithm. The sparsely
adaptive estimation algorithm is proposed. The compressed sampling matching 
tracking algorithm supports the set selection and culling atomic standards to 
improve. The sparse step size adaptive compressed sampling matching tracking 
algorithm is proposed. The improved algorithm selects the sparsely as the step 
size to select the support set atom, and the maximum correlation value. Half of 
the threshold culling algorithm supports the concentration of excess atoms. The 
experimental results show that the improved algorithm has better power and 
lower image reconstruction error under the same sparsely criterion, and has 
higher image reconstruction quality and visual effects.},
DOI = {10.32604/jiot.2020.09117}
}