Presented by: Mingyuan Zhou Duke University, ECE Feb 22, 2013 Large Scale Variational Bayesian Inference for Structured Scale Mixture Models Young Jun Ko and Matthias Seeger ICML 2012 Presented by: Mingyuan Zhou Duke University, ECE Feb 22, 2013

Introduction Natural image statistics exhibit hierarchical dependencies across multiple scales. Non-factorial latent tree models. 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.

Structured Image Model Impose sparsity

Structured Image Model Non-factorial scale mixture model: Output:

Example

Large Scale Variational Inference Due to strong dependencies between components of u and s, factorial assumption might be restrictive. Iterative decoupling Decouple Decouple mean and covariance components of Prior:

Large Scale Variational Inference VB

Large Scale Variational Inference

Image denoising

Image inpainting

Conclusions