.png){kind=icon}
.png){kind=icon}
Return
Improved sparsity adaptive matching pursuit algorithm based on compressed sensing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.displa.2023.102396.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Traditional greedy algorithms need to know the sparsity of the signal in advance, while the sparsity adaptive matching pursuit algorithm avoids this problem at the expense of computational time. To overcome these problems, this paper proposes a variable step size sparsity adaptive matching pursuit (SAMPVSS). In terms of how to select atoms, this algorithm constructs a set of candidate atoms by calculating the correlation between the measurement matrix and the residual and selects the atom most related to the residual. In determining the number of atoms to be selected each time, the algorithm introduces an exponential function. At the beginning of the iteration, a larger step is used to estimate the sparsity of the signal. In the latter part of the iteration, the step size is set to one to improve the accuracy of reconstruction. The simulation results show that the proposed al-gorithm has good reconstruction effects on both one-dimensional and two-dimensional signals.
Keywords:
Compressed sensing
Image reconstruction
Greedy algorithm
Sparsity adaptation