Large Scale Variational Bayesian Inference for Structured Scale Mixture   Models
  Natural image statistics exhibit hierarchical dependencies across multiple scales. Representing such prior knowledge in non-factorial latent tree models can boost performance of image denoising, inpainting, deconvolution or reconstruction substantially, beyond standard factorial "sparse" methodology. We derive a large scale approximate Bayesian inference algorithm for linear models with non-factorial (latent tree-structured) scale mixture priors. Experimental results on a range of denoising and inpainting problems demonstrate substantially improved performance compared to MAP estimation or to inference with factorial priors. 
Young Jun Ko (Ecole Polytechnique Federale de Lausanne), Matthias   Seeger (Ecole Polytechnique Federale de Lausanne)
cs.CV cs.LG stat.ML