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Found 9 papers, 6 papers with code
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.
DeepCEL0 for 2D Single Molecule Localization in Fluorescence Microscopy
In fluorescence microscopy, Single Molecule Localization Microscopy (SMLM) techniques aim at localizing with high precision high density fluorescent molecules by stochastically activating and imaging small subsets of blinking emitters.
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.
Recursive Deep Prior Video: a Super Resolution algorithm for Time-Lapse Microscopy of organ-on-chip experiments
Biological experiments based on organ-on-chips (OOCs) exploit light Time-Lapse Microscopy (TLM) for a direct observation of cell movement that is an observable signature of underlying biological processes.
On the inverse Potts functional for single-image super-resolution problems
We consider a variational model for single-image super-resolution based on the assumption that the image gradient of the target image is sparse.
Image and Video Processing Numerical Analysis Numerical Analysis 65F22, 65K10 G.1.6; G.1.10; I.4.3; I.4.4; I.4.5; I.4.6
Preliminary analysis of COVID-19 spread in Italy with an adaptive SEIRD model
In this paper we propose a Susceptible-Infected-Exposed-Recovered-Dead (SEIRD) differential model for the analysis and forecast of the COVID-19 spread in some regions of Italy, using the data from the Italian Protezione Civile from February 24th 2020.
A fast nonconvex Compressed Sensing algorithm for highly low-sampled MR images reconstruction
In this paper we present a fast and efficient method for the reconstruction of Magnetic Resonance Images (MRI) from severely under-sampled data.
