Ko & Seeger ICML 2012 Large Scale VBI 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 nonfactorial (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.