  In this letter, the problem of sparse signal reconstruction from one bit
compressed sensing measurements is investigated. To solve the problem, a
variational Bayes framework with a new statistical multivariate model is used.
The dependency of the wavelet decomposition coefficients is modeled with a
multivariate Gaussian copula. This model can separate marginal structure of
coefficients from their intra scale dependency. In particular, the drawable
Gaussian vine copula multivariate double Lomax model is suggested. The
reconstructed signal is derived by variational Bayes algorithm which can
calculate closed forms for posterior of all unknown parameters and sparse
signal. Numerical results illustrate the effectiveness of the proposed model
and algorithm compared with the competing approaches in the literature.
