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.
- URL: http://techtalks.tv/talks/large-scale-variational-bayesian-inference-for-structured-scale-mixture-models/57525/
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Record created on 2012-05-17, modified on 2016-08-09