Image superresolution

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• Shin-Ichi Maeda
• Monday 25 February 2008, 11:15-12:15
• TCM Seminar Room, Cavendish Laboratory, Department of Physics.

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.

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