Variational image reconstruction from X-ray micro-tomography data with mixed noisePP

X-ray Computed Tomography (CT) is a popular method with applications in medicine and engineering. However raw CT data usually contain noise that makes the reconstruction procedure mathematically challenging. Therefore the aim of this work is to build an efficient variational image reconstruction method that models the noise effect in the CT data and reconstruct them efficiently. In particular, we present a reconstruction model for mixed Gaussian-Poisson distributions and a new iterative scheme that solves it.

This is poster number 19 in Poster Session

George Papanikos (University of Nottingham)
Yves van Gennip (University of Nottingham)
Pearl Agyakwa (Faculty of Engineering, University of Nottingham)
computed tomography, image reconstruction, inverse problems