1. 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

2. 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.

3. Structured Image Model
Impose sparsity

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

5. Example

7. 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:

8. Large Scale Variational InferenceVB

9. Large Scale Variational Inference

10. Image denoising

11. Image inpainting

14. Conclusions