Image deconvolution using tree-structured Bayesian group sparse modeling

In this paper, we propose to incorporate wavelet tree structures into a recently developed wavelet modeling method, called VBMM. We show that, using overlapped groups, tree-structured modeling can be integrated into the high-performance non-convex sparsity-inducing VBMM method, and can achieve significant performance gains over the coefficient-sparse version of the algorithm.