Search Results for author:
Found 10 papers, 3 papers with code
Inverse Problems with Learned Forward Operators
The first one is completely agnostic to the forward operator and learns its restriction to the subspace spanned by the training data.
Unsupervised Learning of the Total Variation Flow
Inspired by and extending the framework of physics-informed neural networks (PINNs), we propose the TVflowNET, an unsupervised neural network approach, to approximate the solution of the TV flow given an initial image and a time instance.
Image reconstruction in light-sheet microscopy: spatially varying deconvolution and mixed noise
Then, we formulate a variational model that accounts for the combination of Poisson and Gaussian noise through a data fidelity term consisting of the infimal convolution of the single noise fidelities, first introduced in L. Calatroni et al. "Infimal convolution of data discrepancies for mixed noise removal", SIAM Journal on Imaging Sciences 10. 3 (2017), 1196-1233.
Two-layer neural networks with values in a Banach space
We study two-layer neural networks whose domain and range are Banach spaces with separable preduals.
Deeply Learned Spectral Total Variation Decomposition
To the best of our knowledge, this is the first approach towards learning a non-linear spectral decomposition of images.
Variational regularisation for inverse problems with imperfect forward operators and general noise models
We study variational regularisation methods for inverse problems with imperfect forward operators whose errors can be modelled by order intervals in a partial order of a Banach lattice.
Numerical Analysis Numerical Analysis Optimization and Control 47A52, 65J20, 65J22, 65K10
Total Variation Regularisation with Spatially Variable Lipschitz Constraints
We introduce a first order Total Variation type regulariser that decomposes a function into a part with a given Lipschitz constant (which is also allowed to vary spatially) and a jump part.
Numerical Analysis Numerical Analysis 65J20, 65J22, 68U10, 94A08
A total variation based regularizer promoting piecewise-Lipschitz reconstructions
We introduce a new regularizer in the total variation family that promotes reconstructions with a given Lipschitz constant (which can also vary spatially).
Image reconstruction with imperfect forward models and applications in deblurring
In this approach, errors in the data and in the forward models are described using order intervals.
Diffusion tensor imaging with deterministic error bounds
Errors in the data and the forward operator of an inverse problem can be handily modelled using partial order in Banach lattices.
