University of Cambridge > Talks.cam > Machine Learning Journal Club > Image superresolution

Image superresolution

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact David MacKay.

The goal of superresolution is to generate a high-resolution image by integrating low-resolution degraded observed images. We propose a Bayesian approach whose prior is modeled as a compound Gaussian Markov random field (MRF). This approach is advantageous in preserving discontinuity in the original image, in comparison to the existing single-layer Gaussian MRF models. To perform the computation efficiently, we introduce a variatonal EM algorithm.

This talk is part of the Machine Learning Journal Club series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2024 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity