Prior-based privacy-assured compressed sensing scheme in cloud.

Compressed sensing (CS) is a popular signal processing technique. However, some of its performances still need to be improved for possible secure visual applications, including the optimization of the measurement matrix, privacy assurance, and the sparse recovery performance. To this end, we present prior-based measurement matrix design and sparse recovery algorithm for privacy-assured CS scheme in the cloud. More specifically, the measurement matrix is modeled by minimizing a Frobenius difference between the identity matrix and the Gram of the weighted sensing matrix. The gradient descent method is employed to derive the prior probability-weighted measurement matrix. Further, privacy-assured CS can be achieved by using within-row permutation and chaotic matrices. Finally, we also employ the prior information to enhance the accuracy of the sparse recovery algorithm by using prior probability-weighted orthogonal matching pursuit. Theoretical analyses and simulation results demonstrate that the proposed scheme can optimize measurement matrix, achieve privacy-assured CS, and improve sparse recovery performance. [ABSTRACT FROM AUTHOR]