In this paper, we propose a variation model
which takes advantage of the wavelet tight frame and nonconvex shrinkage
penalties for compressed sensing recovery. We address the proposed
optimization problem by introducing a adjustable parameter and a firm
thresholding operations. Numerical experiment results show that the proposed
method outperforms some existing methods in terms of the convergence speed and
JEL classification numbers: 68U10, 65K10, 90C25, 62H35.
Keywords: Compressed Sensing, Nonconvex, Firm thresholding, Wavelet