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

Multivariate Copula Spatial Dependency in One Bit Compressed Sensing