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Found 8 papers, 6 papers with code
A variable metric proximal stochastic gradient method: an application to classification problems
To control the variance of the objective's gradients, we use an automatic sample size selection along with a variable metric to precondition the stochastic gradient directions.
Constrained and unconstrained deep image prior optimization models with automatic regularization
Numerical results demonstrate the robustness with respect to image content, noise levels and hyperparameters of the proposed models on both denoising and deblurring of simulated as well as real natural and medical images.
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.
On the geometric and Riemannian structure of the spaces of group equivariant non-expansive operators
Group equivariant non-expansive operators have been recently proposed as basic components in topological data analysis and deep learning.
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.
Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM
In the last decades, unsupervised deep learning based methods have caught researchers attention, since in many real applications, such as medical imaging, collecting a great amount of training examples is not always feasible.
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
