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—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(More)
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(More)
a r t i c l e i n f o a b s t r a c t Article history: Available online xxxx Keywords: Image restoration Wavelet group-sparse modeling Variational Bayesian inference Majorization minimization Dual-tree complex wavelets In this work, we present a recent wavelet-based image restoration framework based on a group-sparse Gaussian scale mixture model. A(More)
In this paper, we propose a new Markov-tree Bayesian modeling of wavelet coefficients. Based on a group-sparse GSM model with 2-layer cascaded Gamma distributions for the variances, the proposed method effectively exploits both intrascale and interscale relationships across wavelet subbands. To determine the posterior distribution, we apply Variational(More)
This paper describes a complete low-complexity imaging system based on a single MEMS scanning mirror and a single photodetector, together with customized image enhancement algorithms based on sparse signal representation. Due to very low complexity of our developped optical setup for image acquisition, resulting images suffer visible artifacts. We propose(More)
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