Variational image reconstruction methods for tomography - PET and transmission CT with mixed Poisson-Gaussian noise - Papanikos, G (2020)
This thesis focuses on total variation based variational image reconstruction models that arise in linear and non-linear inverse problems with measurements corrupted by mixed Poisson and Gaussian noise. An inverse problem can be described as identifying model parameters given the observed measurements. More specifically in this work, we consider inverse problems arises in positron emission tomography and transmission computed tomography. For these problems in the literature, a single noise assumption on the measurements is usually taken. Here, in both tomographic scenarios, we consider measurements corrupted not only by Poisson but also by Gaussian noise.