Compressive Imaging Using Approximate Message Passing and a Markov-Tree Prior

Subhojit Som, Philip Schniter
2012 IEEE Transactions on Signal Processing  
We propose a novel algorithm for compressive imaging that exploits both the sparsity and persistence across scales found in the 2D wavelet transform coefficients of natural images. Like other recent works, we model wavelet structure using a hidden Markov tree (HMT) but, unlike other works, ours is based on loopy belief propagation (LBP). For LBP, we adopt a recently proposed "turbo" message passing schedule that alternates between exploitation of HMT structure and exploitation of
more » ... surement structure. For the latter, we leverage Donoho, Maleki, and Montanari's recently proposed approximate message passing (AMP) algorithm. Experiments with a large image database suggest that, relative to existing schemes, our turbo LBP approach yields state-of-the-art reconstruction performance with substantial reduction in complexity.
doi:10.1109/tsp.2012.2191780 fatcat:anzbl343hfgfxejxdg7qqigckm