1 research outputs found
Compressed Sensing with Probability-based Prior Information
This paper deals with the design of a sensing matrix along with a sparse
recovery algorithm by utilizing the probability-based prior information for
compressed sensing system. With the knowledge of the probability for each atom
of the dictionary being used, a diagonal weighted matrix is obtained and then
the sensing matrix is designed by minimizing a weighted function such that the
Gram of the equivalent dictionary is as close to the Gram of dictionary as
possible. An analytical solution for the corresponding sensing matrix is
derived which leads to low computational complexity. We also exploit this prior
information through the sparse recovery stage and propose a probability-driven
orthogonal matching pursuit algorithm that improves the accuracy of the
recovery. Simulations for synthetic data and application scenarios of
surveillance video are carried out to compare the performance of the proposed
methods with some existing algorithms. The results reveal that the proposed CS
system outperforms existing CS systems.Comment: 13 pages, 9 figure