We propose a Bayesian expectation-maximization (EM) algorithm for reconstructing structured approximately
sparse signals via belief propagation. The measurements follow an underdetermined linear model where the
regression-coefficient vector is the sum of an unknown approximately sparse signal and a zero-mean white Gaussian
noise with an unknown variance. The signal is composed of large- and small-magnitude components identified
by binary state variables whose probabilistic dependence structure is described by a hidden Markov tree (HMT).
Gaussian priors are assigned to the signal coefficients given their state variables and the Jeffreys’ noninformative
prior is assigned to the noise variance. Our signal reconstruction scheme is based on an EM iteration that aims
at maximizing the posterior distribution of the signal and its state variables given the noise variance. We employ
a max-product algorithm to implement the maximization (M) step of our EM iteration. The noise variance is
a regularization parameter that controls signal sparsity. We select the noise variance so that the corresponding
estimated signal and state variables (obtained upon convergence of the EM iteration) have the largest marginal
posterior distribution. Our numerical examples show that the proposed algorithm achieves better reconstruction
performance compared with the state-of-the-art methods.