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Fast L0-based image deconvolution with variational Bayesian inference and majorization-minimization

In this paper, we propose a new wavelet-based image deconvolution algorithm to restore blurred images based on a Gaussian scale mixture model within the variational Bayesian framework. Our sparsity-regularized model approximates an l0 norm by reweighting an l2 norm iteratively. We derive a hierarchial Bayesian estimation with the use of subband adaptive majorization-minimization which simplifies computation of the posterior distribution, and has been shown to find good solutions in the non-convex search space. The proposed method is flexible enough to incorporate group-sparse optimization.