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Non-Smooth-Norm Image Reconstruction from Noisy Data

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If you have a question about this talk, please contact Zoubin Ghahramani.

The Talk presents a new approach to the problem of reconstructing Images from noisy data by means of Analytic Inversion Formlae. In particular, we focus reconstruction of PET /SPECT images from Real Radon Data. We will also outline rigorous mathematical formalism for the understanding of some basic mechanisms which allow the use of deterministic Inversion Algorithms to REAL data, i.e. data corrupted sampling deficiencies, combined with Gaussian and Non-Gaussian noise. Some of these results can be used as the basis for a more efficient paradigm of Transform Inversion from noisy, even critically under-sampled data.

This talk is part of the Machine Learning @ CUED series.

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