Publication

Large Scale Variational Bayesian Inference for Structured Scale Mixture Models

Matthias Seeger, Young Jun Ko
2012
Conference Papers

Abstract

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.

Official source

https://infoscience.epfl.ch/entities/publication/de07eb2c-4c9d-4dd8-9153-ef6381acd6c7

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Matthias Seeger, Young Jun Ko
2012
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/de07eb2c-4c9d-4dd8-9153-ef6381acd6c7

About this result

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