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Found 5 papers, 3 papers with code
Space-Variant Total Variation boosted by learning techniques in few-view tomographic imaging
This paper focuses on the development of a space-variant regularization model for solving an under-determined linear inverse problem.
Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc
The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disciplines.
Ambiguity in solving imaging inverse problems with deep learning based operators
Numerical experiments are performed to verify the accuracy and stability of the proposed approaches for image deblurring when unknown or not-quantified noise is present; the results confirm that they improve the network stability with respect to noise.
To be or not to be stable, that is the question: understanding neural networks for inverse problems
The solution of linear inverse problems arising, for example, in signal and image processing is a challenging problem since the ill-conditioning amplifies, in the solution, the noise present in the data.
Plug-and-Play gradient-based denoisers applied to CT image enhancement
In particular, we consider different schemes encompassing external and internal denoisers as priors, defined on the image gradient domain.
