An Educated Warm Start for Deep Image Prior-Based Micro CT Reconstruction
@article{Barbano2021AnEW,
title={An Educated Warm Start for Deep Image Prior-Based Micro CT Reconstruction},
author={Riccardo Barbano and Johannes Leuschner and Maximilian Schmidt and Alexander Denker and Andreas Hauptmann and Peter Maass and Bangti Jin},
journal={IEEE Transactions on Computational Imaging},
year={2021},
volume={8},
pages={1210-1222}
}Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks. DIP represents the image to be recovered as the output of a deep convolutional neural network, and learns the network's parameters such that the model output matches the corrupted observation. Despite its impressive reconstructive properties, the approach is slow when compared to supervisedly learned, or traditional reconstruction techniques. To address the computational challenge…
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2 Citations
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior
- Computer Science
- 2022
This paper develops a method, termed as the linearised deep image prior (DIP), to estimate the uncertainty associated with reconstructions produced by the DIP with total variation regularisation (TV), and endow it with conjugate Gaussian-linear model type error-bars computed from a local linearisation of the neural network around its optimised parameters.
Unsupervised denoising for sparse multi-spectral computed tomography
- Computer ScienceArXiv
- 2022
This work investigates the suitability of learning-based improvements to the challenging task of obtaining high-quality reconstructions from sparse measurements for a 64-channel PCD-CT and proposes an unsupervised denoising and artefact removal approach.
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