3 citations · 4 across the 3 of their papers we have counts for
3 papers
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction
Marco Nittscher, Michael Lameter, Riccardo Barbano +3
The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless. The DIP overfits to noise if not ear…
Image Reconstruction via Deep Image Prior Subspaces
Riccardo Barbano, Javier Antorán, Johannes Leuschner +3
Deep learning has been widely used for solving image reconstruction tasks but its deployability has been held back due to the shortage of high-quality training data. Unsupervised l…
Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior
Riccardo Barbano, Johannes Leuschner, Javier Antorán +2
We investigate adaptive design based on a single sparse pilot scan for generating effective scanning strategies for computed tomography reconstruction. We propose a novel approach…